Anomaly Detection in Automatic Meter Intelligence System Using Positive Unlabeled Learning and Multiple Symbolic Aggregate Approximation.

With the development of automatic electrical devices in smart grids, the data generated by time and transmitted are vast and thus impossible to control consumption by humans. The problem of abnormal detection in power consumption is crucial in monitoring and controlling smart grids. This article proposes the detection of electrical meter anomalies by detecting abnormal patterns and learning unlabeled data.

A new primate from the late Eocene of Vietnam illuminates unexpected strepsirrhine diversity and evolution in Southeast Asia.

Sivaladapidae is a poorly known Asian strepsirrhine family originally discovered in Miocene sediments of the Indian subcontinent. Subsequent research has considerably increased the diversity, temporal range, and geographical distribution of this group, now documented from China, Thailand, Myanmar, Pakistan, and India and whose earliest representatives date back to the Middle Eocene. We present here a new taxon of sivaladapid from the Na Duong coal mine in the Latest Middle Eocene-Late Eocene of Vietnam.