Application of observation operators for field scale soil moisture averages and variances in agricultural landscapes

Scale difference between in situ and remotely sensed soil moisture observations and model grid size has been an issue for validation of remote sensing data, soil moisture data assimilation and calibration of hydrologic models. This study aims to link two different scales of soil moisture estimates by upscaling single point measurements to field averages for representing field-scale agricultural areas (∼2 ha) located within the Upper Cedar Creek Watershed in northeastern Indiana. Several statistical methods, mainly focusing on cumulative distribution function (CDF) matching, are tested to upscale point measurements to spatially representative soil moisture. These transforming equations are termed observation operators. The CDF matching is found to be the most successful upscaling method followed by the mean relative difference method using temporally stable point measurements. This study also tests the temporal and spatial (horizontal and vertical) transferability of the different observation operators. Results indicate that the two observation operators from the CDF matching approach and the mean relative difference method using a temporally stable location are transferable in space, but not in time. Rainfall characteristic is most likely the dominant factor affecting the success of observation operator transferability. In addition, the CDF matching approach is found to be an effective method to deduce spatial variability (standard deviation) of soil moisture from single point measurements

A new 111 type iron pnictide superconductor LiFeP

A new iron pnictide LiFeP superconductor was found. The compound crystallizes into a Cu2Sb structure containing an FeP layer showing superconductivity with maximum Tc of 6K. This is the first 111 type iron pnictide superconductor containing no arsenic. The new superconductor is featured with itinerant behavior at normal state that could helpful to understand the novel superconducting mechanism of iron pnictide compounds.Comment: 3 figures + 1 tabl

Superconductivity in Co-doped LaFeAsO

Here we report the synthesis and basic characterization of LaFe1-xCoxAsO for several values of x. The parent phase LaFeAsO orders antiferromagnetically (TN ~ 145 K). Replacing Fe with Co is expected to both electron dope the system and introduce disorder in the FeAs layer. For x = 0.05 antiferromagnetic order is destroyed and superconductivity is observed at Tconset = 11.2 K. For x = 0.11 superconductivity is observed at Tc(onset) = 14.3 K, and for x = 0.15 Tc = 6.0 K. Superconductivity is not observed for x = 0.2 and 0.5, but for x = 1, the material appears to be ferromagnetic (Tc ~ 56 K) as judged by magnetization measurements. We conclude that Co is an effective dopant to induce superconductivity. Somewhat surprisingly, the system appears to tolerate considerable disorder in the FeAs planes.Comment: 19 pages, 9 figure

The influence of self-citation corrections on Egghe's g index

The g index was introduced by Leo Egghe as an improvement of Hirsch's index h for measuring the overall citation record of a set of articles. It better takes into account the highly skewed frequency distribution of citations than the h index. I propose to sharpen this g index by excluding the self-citations. I have worked out nine practical cases in physics and compare the h and g values with and without self-citations. As expected, the g index characterizes the data set better than the h index. The influence of the self-citations appears to be more significant for the g index than for the h index.Comment: 9 pages, 2 figures, submitted to Scientometric

Batch Mode Active Learning with Applications to Text Categorization and Image Retrieval

Singapore Ministry of Education Academic Research Fund Tier

Batch Mode Active Learning with Applications to Text Categorization and Image Retrieval

Singapore Ministry of Education Academic Research Fund Tier

Closed-loop optimization of fast-charging protocols for batteries with machine learning.

Simultaneously optimizing many design parameters in time-consuming experiments causes bottlenecks in a broad range of scientific and engineering disciplines1,2. One such example is process and control optimization for lithium-ion batteries during materials selection, cell manufacturing and operation. A typical objective is to maximize battery lifetime; however, conducting even a single experiment to evaluate lifetime can take months to years3-5. Furthermore, both large parameter spaces and high sampling variability3,6,7 necessitate a large number of experiments. Hence, the key challenge is to reduce both the number and the duration of the experiments required. Here we develop and demonstrate a machine learning methodology  to efficiently optimize a parameter space specifying the current and voltage profiles of six-step, ten-minute fast-charging protocols for maximizing battery cycle life, which can alleviate range anxiety for electric-vehicle users8,9. We combine two key elements to reduce the optimization cost: an early-prediction model5, which reduces the time per experiment by predicting the final cycle life using data from the first few cycles, and a Bayesian optimization algorithm10,11, which reduces the number of experiments by balancing exploration and exploitation to efficiently probe the parameter space of charging protocols. Using this methodology, we rapidly identify high-cycle-life charging protocols among 224 candidates in 16 days (compared with over 500 days using exhaustive search without early prediction), and subsequently validate the accuracy and efficiency of our optimization approach. Our closed-loop methodology automatically incorporates feedback from past experiments to inform future decisions and can be generalized to other applications in battery design and, more broadly, other scientific domains that involve time-intensive experiments and multi-dimensional design spaces