The goal of maritime traffic management is to provide a safe and efficient maritime environment for different type of vessels facilitating port logistics and supply chain business. However, current maritime traffic management mainly relies on the massive individual vessel’s data for decision making. Lack of macro-level understanding of vessel crowd movement around port challenges maritime safety and traffic efficiency. In this paper, we describe a spatio-temporal data mining method to discover crowd movement patterns of vessels from their short-term history data. The method first captures vessels’ crowd movement features by building vessels’ tracklets with their speed and location. A movement vector clustering algorithm is developed to find different travel behaviors for different group of vessels. With nonparametric regression on the classified vessel movement vectors which represent the crowd travel behaviors, an overall vessel movement pattern can then be discovered. In this research, we tested real trajectory data of vessels near Singapore ports. Comparing with the actual massive vessel movement data, we found that this method was able to extract vessels’ crowd movement information. The hotspots on risk area in terms of vessel traffic and speed can be identified. The method can be used to provide decision-making support for maritime traffic management.
Jiang Xiao, Yan-mei Li, Ying-xiu Huang, Wen Zhang, Wen-jing Su, Wei Zhang, Ning Han, Di Yang, Xin Li, Gui-ju Gao and Hong-xin Zhao
Objective The aim of the study was to evaluate the characteristics of HIV drug-genotypic resistance among patients taking first-line ARV regimens using polymerase chain reaction and sequencing, and guide to design optimal ARV regimens for these patients.
Methods HIV reverse transcriptase-encoded gene was amplified with RT-PCR and amplified PCR products were aligned and comparatively analyzed with HIV resistance database to find drug-resistance mutations.
Results Twenty-eight PCR products were amplified and sequenced successfully in 30 serum samples of recruited HIV-infected patients with virologic failure. The resistance rate was 96%, mutations in NRT region were found in 26 patients (93%), while mutations in NNRT region were found in 27 patients (96%). M184V was the most common mutation (86%), K65R was selected in 14% of recruited individuals and TAMs occurred in 50% of patients, which resulted in resistance to NRTIs. Y181C and V179D were the most common mutations in NNRTIs and prevalence was 43% (12/28) and 36% (10/28), respectively, which resulted in cross-resistance to NNRTIs due to low-genetic barrier.
Conclusions Virologic failure may occur in long-term administration of first-line ARV regimens, and drugresistance mutations can be found in these patients, which resulted in resistance to first-line ARV regimens. We emphasized that HIV viral load assay and resistance assay were important tools to guide healthcare workers to design an optimal second-line ARV regimens for HAART-experienced individuals with virologic failure.