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Thirty-four alcoholics were treated with acupuncture to the ear and the body in a randomized single-blind placebo-controlled design over 14 days. Orthodox points and placebo needles to orthodox points were used daily for a total of 10 treatments starting on the first day of admission as add-on therapy to standard medication with carbamazepine. The primary outcome was the Clinical Institute Withdrawal Assessment (CIWA-Ar-scale) assessed on days 1-6, 9 and 14. No initial differences were found regarding sociodemographic data, drinking history and alcohol-related data, indicating successful randomization. Longitudinal analysis of the Clinical Institute Withdrawal Assessment (CIWA-Ar-scale) data showed that patients assigned to acupuncture had a general tendency towards better outcome results and significantly fewer withdrawal symptoms on day 14 (Wilcoxon-W=177.500, Z=-2.009, p = 0.045). No significant differences were found in the Beck Depression Inventory (BDI), State-Trait Anxiety Inventory (STAI X1 and X2) and Eigenschaftswoerterliste (EWL S60). We conclude that acupuncture as an adjunctive treatment to carbamazepine medication shows promise for the treatment of alcohol withdrawal symptoms. Further investigation of this treatment modality appears to be warranted.

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Abstract:With the rise of the web 2.0 and the Internet of things, it has become feasible to track all kinds of information over time, in particular fine-grained user activities and sensor data on their environment and even their biometrics. However, while efficiency remains mandatory for any application trying to cope with huge amounts of data, only part of the potential of today's Big Data repositories can be exploited using traditional batch-oriented approaches as the value of data often decays quickly and high latency becomes unacceptable in some applications. In the last couple of years, several distributed data processing systems have emerged that deviate from the batchoriented approach and tackle data items as they arrive, thus acknowledging the growing importance of timeliness and velocity in Big Data analytics. In this article, we give an overview over the state of the art of stream processors for low-latency Big Data analytics and conduct a qualitative comparison of the most popular contenders, namely Storm and its abstraction layer Trident, Samza and Spark Streaming. We describe their respective underlying rationales, the guarantees they provide and discuss the trade-offs that come with selecting one of them for a particular task.

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