Abstract. Developing a hydrological forecasting model based on past records is crucial to 23 effective hydropower reservoir management and scheduling. Traditionally, time series analysis and 24 modeling is used for building mathematical models to generate hydrologic records in hydrology 25 and water resources. Artificial intelligence (AI), as a branch of computer science, is capable of 26 analyzing long-series and large-scale hydrological data. In recent years, it is one of front issues to 27 apply AI technology to the hydrological forecasting modeling. In this paper, autoregressive 28 moving-average (ARMA) models, artificial neural networks (ANNs) approaches, adaptive 29 neural-based fuzzy inference system (ANFIS) techniques, genetic programming (GP) models and 30 support vector machine (SVM) method are examined using the long-term observations of monthly 31 river flow discharges. The four quantitative standard statistical performance evaluation measures, 32 the coefficient of correlation (R), Nash-Sutcliffe efficiency coefficient (E), root mean squared 33 error (RMSE), mean absolute percentage error (MAPE), are employed to evaluate the 34 performances of various models developed. Two case study river sites are also provided to 35 illustrate their respective performances. The results indicate that the best performance can be 36 obtained by ANFIS, GP and SVM, in terms of different evaluation criteria during the training and 37 validation phases. 38 39

Independent component analysis (ICA) utilizing prior information, also called semiblind ICA, has demonstrated considerable promise in the analysis of functional magnetic resonance imaging (fMRI). So far, temporal information about fMRI has been used in temporal ICA or spatial ICA as additional constraints to improve estimation of task-related components. Considering that prior information about spatial patterns is also available, a semiblind spatial ICA algorithm utilizing the spatial information was proposed within the framework of constrained ICA with fixed-point learning. The proposed approach was first tested with synthetic fMRI-like data, and then was applied to real fMRI data from 11 subjects performing a visuomotor task. Three components of interest including two task-related components and the “default mode” component were automatically extracted, and atlas-defined masks were used as the spatial constraints. The default mode network, a set of regions that appear correlated in particular in the absence of tasks or external stimuli and is of increasing interest in fMRI studies, was found to be greatly improved when incorporating spatial prior information. Results from simulation and real fMRI data demonstrate that the proposed algorithm can improve ICA performance compared to a different semiblind ICA algorithm and a standard blind ICA algorithm.

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Digital nucleic acid amplification provides unprecedented opportunities for absolute nucleic acid quantification by counting of single molecules. This technique is useful for molecular genetic analysis in cancer, stem cell, bacterial, non-invasive prenatal diagnosis in which many biologists are interested. This paper describes a self-priming compartmentalization (SPC) microfluidic chip platform for performing digital loop-mediated amplification (LAMP). The energy for the pumping is pre-stored in the degassed bulk PDMS by exploiting the high gas solubility of PDMS; therefore, no additional structures other than channels and reservoirs are required. The sample and oil are sequentially sucked into the channels, and the pressure difference of gas dissolved in PDMS allows sample self-compartmentalization without the need for further chip manipulation such as with pneumatic microvalves and control systems, and so on. The SPC digital LAMP chip can be used like a 384-well plate, so, the world-to-chip fluidic interconnections are avoided. The microfluidic chip contains 4 separate panels, each panel contains 1200 independent 6 nL chambers and can be used to detect 4 samples simultaneously. Digital LAMP on the microfluidic chip was tested quantitatively by using β-actin DNA from humans. The self-priming compartmentalization behavior is roughly predictable using a two-dimensional model. The uniformity of compartmentalization was analyzed by fluorescent intensity and fraction of volume. The results showed that the feasibility and flexibility of the microfluidic chip platform for amplifying single nucleic acid molecules in different chambers made by diluting and distributing sample solutions. The SPC chip has the potential to meet the requirements of a general laboratory: power-free, valve-free, operating at isothermal temperature, inexpensive, sensitive, economizing labour time and reagents. The disposable analytical devices with appropriate air-tight packaging should be useful for point-of-care, and enabling it to become one of the common tools for biology research, especially, in point-of-care testing.

Dissolved inorganic carbon (DIC), total alkalinity (TAlk), pH, and dissolved oxygen (DO) were determined in the Mississippi River plume during five cruises conducted in the spring, summer, and fall. In contrast to many other large rivers, both DIC and TAlk were higher in river water than in seawater. Substantial losses of DIC, relative to TAlk, occurred within the plume, particularly at intermediate salinities. DIC removal was accompanied by high DO, high pH, and nutrient depletion, and was attributed to high phytoplankton production. As a result, the carbonate saturation in the plume became much higher than in ocean and river waters. A mixing model was used to determine DIC removal. We provide evidence that the use of a two-end-member (river and ocean) mixing model was valid during late summer and fall (low discharge period). However, for other periods we used salinity and TAlk to delineate a mixing model that included two river end members and an ocean end member. Net community production rates in the plume, estimated using a box model, peaked in the summer and were among the highest reported to date for large river plumes. In the summer and fall, biological production in the river plume consumed a majority of the available nutrients, whereas during the spring only a small fraction of the available nutrients were consumed in the plume. Biological production was the dominant process influencing pH and carbonate saturation state along the river-ocean gradient, whereas physicochemical dynamics of mixing played an important role in controlling the TAlk and DIC distributions of this large river plume.

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[1] To characterize atmospheric dissolvable iron over the Southern Ocean (SO) and coastal East Antarctica (CEA), bulk and size-segregated (0.056-18 μm in diameter) aerosols were collected from 34°S, 109°E to 69°S, 76°E and between 69°S, 76°E and 66°S, 110°E ) over CEA; the total dissolvable Fe followed the same trend. Over the SO, a single-peak size distribution of Fe(II) existed. Over CEA, a bimodal size distribution of Fe(II) appeared, with the first peak at 0.32-0.56 μm and the second peak at 5.6-10 μm. Higher Fe concentrations over CEA than over the SO and the existence of coarse mode Fe(II) over CEA suggest potential dust sources in Antarctica. The fractional Fe(II) solubility ranged from 0.58% to 6.5% and decreased with total Fe concentration increase. The estimated atmospheric fluxes of Fe (II)