Chloroquine-resistant Plasmodium vivax has not yet occurred in Vietnam. The efficacy of artemisinin for P. vivax was not established. We conducted a double-blind randomized study involving 240 inpatients with P. vivax malaria who received artemisinin (40 mg/kg over 3 days) plus placebo chloroquine (Art) or chloroquine (25 mg/kg over 3 days) plus placebo artemisinin (Chl). Patients were followed up with weekly blood smears for 28 days. In each group 113 cases were analysed. All patients recovered rapidly. The median (range) parasite clearance time of regimen Art was 24 h (8-72) and of Chl 24 h (8-64; P = 0.3). Parasites reappeared in two cases in each group on day 14, in eight cases in each group (7%) on day 16 and in 25 (23%) and 18 (16%) cases, respectively, at the end of 4-week follow-up (P = 0.3). The population parasite clearance curve followed a mono-exponential decline. The parasite reduction ratio per 48 h reproduction cycle was 2.3 x 104 for both regimens. We conclude that artemisinin and chloroquine are equally effective in the treatment of P. vivax infections in Vietnam. Reappearance of parasites before day 16 (7%) suggests the emergence of chloroquine resistance. Three days of artemisinin monotherapy does not prevent recrudescence.

Wireless localization has a great importance in a variety of areas including commercial, service, and military positioning and tracking systems. In harsh indoor environments, it is hard to localize an agent with high accuracy due to nonline-of-sight (NLOS) radio blockage or insufficient information from anchors. Therefore, NLOS identification and mitigation are highlighted as an effective way to improve the localization accuracy. In this paper, we develop a robust and efficient algorithm to enhance the accuracy for (ultrawide bandwidth) time-of-arrival localization through identifying and mitigating NLOS signals with relevance vector machine (RVM) techniques. We also propose a new localization algorithm, called the twostep iterative (TSI) algorithm, which converges fast with a finite number of iterations. To enhance the localization accuracy as well as expand the coverage of a localizable area, we continue to exploit the benefits of RVM in both classification and regression for cooperative localization by extending the TSI algorithm to a centralized cooperation case. For self-localization setting, we then develop a distributed cooperative algorithm based on variational Bayesian inference to simplify message representations on factor graphs and reduce communication overheads between agents. In particular, we build a refined version of Gaussian variational message passing to reduce the computational complexity while maintaining the localization accuracy. Finally, we introduce the notion of a stochastic localization network to verify proposed cooperative localization algorithms. Index Terms-Cooperative localization, IEEE 802.15.4-2011, non-line-of-sight (NLOS), NLOS mitigation, relevance vector machine (RVM), ultrawide bandwidth (UWB), variational message passing (VMP).

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