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Finding promising materials that show large thermoelectricity at low temperatures is crucial for low-temperature refrigeration applications. Here, we report a significantly large thermoelectric power factor ( PF) up to 50 μW cm−1 K−2 at an extremely low temperature of ∼8 K in a quasi-one-dimensional (Q1D) antiferromagnet KMn6Bi5, in which highly electrical conductivity and dramatic enhancement of Seebeck coefficient may favor its occurrence. In addition, a giant Nernst signal has also been detected with a maximum value of 24 μV K−1 T−1 at ∼5 K. All these intriguing characteristics observed in KMn6Bi5 can be attributed to the manifestation of the unusually strong coupling among spin, charge, lattice, and one dimensionality in KMn6Bi5. Our findings provide fundamental insight into the thermal transport in Q1D antiferromagnets and should stimulate further experimental exploration of thermal transport in such Q1D family for possible thermoelectric applications at extremely low temperatures.

Sensor networks open up new opportunities to observe and interact with the physical world around us. Although there have been many advances in sensor network applications and technology, sensor networks still suffer from the major problems of limited energy due to the difficulties of replacing batteries when they ran out. We aim to improve the efficient processing of continuous queries, by which query results have to be generated according to the sampling rate specified by the user for an extended period of time. Two types of continuous queries, namely queries requiring data from all sensor nodes and selected nodes, are considered in this work. We adopt Kalman filter to correct and predict sensing data, which offers opportunities to dynamically adjust the sampling frequency of each sensor. The proposed resampling approach can effectively reduce the energy consumption without sacrificing users' desired accuracy, as evaluated using empirical data collected from a real sensor network.