Ultralow Doppler frequency extraction for the self-mixing dual-frequency laser velocimetry.

In self-mixing dual-frequency laser Doppler velocimetry, the self-mixing Doppler frequency shift of the optical frequency difference is a linear function of the velocity of an external dynamic object; however, it is always ultralow for signal processing. Therefore, an ultralow frequency extraction method based on artificial neural networks (NNs) is presented because NNs can accurately create a fitting function for a Doppler signal and extend the signal to the DC value, increasing the signal length and sampling points without yielding unnecessary influences on the Doppler frequency. We precisely measured Doppler frequencies in the frequency domain with a low sampling rate and calculated the velocities for a target with longitudinal movements. Compared to time-domain extraction, frequency-domain extraction can reflect the complete information of the original Doppler signal. This feature potentially contributes to the signal processing of velocimetry in practical engineering applications.

Computational Approaches to Detect Illicit Drug Ads and Find Vendor Communities Within Social Media Platforms.

The opioid abuse epidemic represents a major public health threat to global populations. The role social media may play in facilitating illicit drug trade is largely unknown due to limited research. However, it is known that social media use among adults in the US is widespread, there is vast capability for online promotion of illegal drugs with delayed or limited deterrence of such messaging, and further, general commercial sale applications provide safeguards for transactions; however, they do not discriminate between legal and illegal sale transactions. These characteristics of the social media environment present challenges to surveillance which is needed for advancing knowledge of online drug markets and the role they play in the drug abuse and overdose deaths. In this paper, we present a computational framework developed to automatically detect illicit drug ads and communities of vendors. The SVM- and CNN- based methods for detecting illicit drug ads, and a matrix factorization based method for discovering overlapping communities have been extensively validated on the large dataset collected from Google+, Flickr and Tumblr. Pilot test results demonstrate that our computational methods can effectively identify illicit drug ads and detect vendor-community with accuracy. These methods hold promise to advance scientific knowledge surrounding the role social media may play in perpetuating the drug abuse epidemic.

Efficient and Exact Local Search for Random Walk Based Top-K Proximity Query in Large Graphs.

Top-k proximity query in large graphs is a fundamental problem with a wide range of applications. Various random walk based measures have been proposed to measure the proximity between different nodes. Although these measures are effective, efficiently computing them on large graphs is a challenging task. In this paper, we develop an efficient and exact local search method, FLoS (Fast Local Search), for top-k proximity query in large graphs. FLoS guarantees the exactness of the solution. Moreover, it can be applied to a variety of commonly used proximity measures. FLoS is based on the no local optimum property of proximity measures. We show that many measures have no local optimum. Utilizing this property, we introduce several operations to manipulate transition probabilities and develop tight lower and upper bounds on the proximity values. The lower and upper bounds monotonically converge to the exact proximity value when more nodes are visited. We further extend FLoS to measures having local optimum by utilizing relationship among different measures. We perform comprehensive experiments on real and synthetic large graphs to evaluate the efficiency and effectiveness of the proposed method.

EINVis: a visualization tool for analyzing and exploring genetic interactions in large-scale association studies.

Epistasis (gene-gene interaction) detection in large-scale genetic association studies has recently drawn extensive research interests as many complex traits are likely caused by the joint effect of multiple genetic factors. The large number of possible interactions poses both statistical and computational challenges. A variety of approaches have been developed to address the analytical challenges in epistatic interaction detection. These methods usually output the identified genetic interactions and store them in flat file formats. It is highly desirable to develop an effective visualization tool to further investigate the detected interactions and unravel hidden interaction patterns. We have developed EINVis, a novel visualization tool that is specifically designed to analyze and explore genetic interactions. EINVis displays interactions among genetic markers as a network. It utilizes a circular layout (specially, a tree ring view) to simultaneously visualize the hierarchical interactions between single nucleotide polymorphisms (SNPs), genes, and chromosomes, and the network structure formed by these interactions. Using EINVis, the user can distinguish marginal effects from interactions, track interactions involving more than two markers, visualize interactions at different levels, and detect proxy SNPs based on linkage disequilibrium. EINVis is an effective and user-friendly free visualization tool for analyzing and exploring genetic interactions. It is publicly available with detailed documentation and online tutorial on the web at http://filer.case.edu/yxw407/einvis/.

[Morphological characteristics of algae floc and its relation with flotation].

Microcysits aeruginosa (MA) was removed by polyaluminium chloride (PAC) in the coagulation-flotation process. The test studied removing effect and morphological characteristics of PAC-MA flocs in the different coagulation conditions and discussed the correlation of them. Fractal dimension was measured by image analysis. The results indicate that the best range in dosage, stirring strength and time of flocculation reaction are respectively 5.6 - 9.8 mg/L Al2O3, 50 - 80 s(-1) and 5 - 8 min in the condition of mixing stirring strength of 500 s(-1), mixing time of 1 min, circulate ratio of 10%. The dosage, stirring strength and time of flocculation reaction influenced the form of flocs remarkably. Moreover the range of fractal dimension D2 is between 1.1688 and 1.2357, and average diameters vary between 300 microm and 500 microm with the best flocculation condition. The flocs with the looser structure, the bigger diameter variation and more branches can adhere to bubbles more easily.

[Treatment of high algae-laden water by step-recycle counter current flotation].

High algae-laden water was treated respectively by single-recycle and step-recycle counter current flotation. The results indicate that step-recycle mode of dissolved air water can strengthen capturing of bubbles and flocs association suspension bed, and extend the collision time of smaller flee and bubble because of reducing the thickness of suspension bed and increasing that of transition bed. Given the condition that other parameters were same, the biggest hydraulic loading of single-recycle could reach 11 m/h, and that of step-recycle could reach 17 m/h which is increased by 50%. Efficiency of treatment was improved by a large margin. The recycle ratio of two releasers and position of the pensile releaser influenced removal effect remarkably. The distance between settled releaser and inlet of raw water is 180 cm, and the range of distance between pensile releaser and inlet of raw water is 60-90 cm. The flow of pensile releaser is 2-3 times as much as settled releaser.