Behavior features of heavy metals in the Haikou Bay waters

Dissolved Cu, Ph, Zn and Cd in the Haikou Bay waters were measured to be respectively in the range concentrations of 0.47-1.16 μg/dm^3, 0. 94-- 2. 36μg/dm^3, 1.28-4.83 μg/dm^3 and 0. 005-0.072μg/dm^3; with respectively average values of 0.78μg/dm^3, 1.36μg/dm^3, 3.14 g/dm^3 and 0. 03 μg/dm^3. Dissolved Cu and Zn concentrations are relatively high at the stations near the Longkun Road Outfall for domestic sewage, the Xiuying Outfall for industry waste water and the Haidian Island Estuary, but dissolved Pb and Cd concentrations are low in these stations. The values in Other stations are comparatively homogenous. Vertical dissolved Cu, Pb and Zn concentrations at the bottom layer are higher than at the surface layer, but dissolved Cd concentration appears to be on the opposite. The measurement results of Cu, Pb, Zn and Cd in suspended particle show that particulate matters in the Haikou Bay seawater play a role in purifying heavy metals. The study on strong complexed form and non-liable form of dissolved copper show that the ratio of strong complexed form and dissolved form is about 85%, and non-liable form is very low with a value lower than 5 nmol/dm^3. Therefore, copper in the Haikou Bay seawater cannot cause influence on marine organisms.

Behavioral Feature and Correlative Detection of Multiple Types of Node in the Internet of Vehicles

Undoubtedly,uncooperative or malicious nodes threaten the safety of Internet of Vehicles(IoV)by destroying routing or data.To this end,some researchers have designed some node detection mechanisms and trust calculating algorithms based on some different feature parameters of IoV such as communication,data,energy,etc.,to detect and evaluate vehicle nodes.However,it is difficult to effectively assess the trust level of a vehicle node only by message forwarding,data consistency,and energy sufficiency.In order to resolve these problems,a novel mechanism and a new trust calculating model is proposed in this paper.First,the four tuple method is adopted,to qualitatively describing various types of nodes of IoV;Second,analyzing the behavioral features and correlation of various nodes based on route forwarding rate,data forwarding rate and physical location;third,designing double layer detection feature parameters with the ability to detect uncooperative nodes and malicious nodes;fourth,establishing a node correlative detection model with a double layer structure by combining the network layer and the perception layer.Accordingly,we conducted simulation experiments to verify the accuracy and time of this detection method under different speed-rate topological conditions of IoV.The results show that comparing with methods which only considers energy or communication parameters,the method proposed in this paper has obvious advantages in the detection of uncooperative and malicious nodes of IoV;especially,with the double detection feature parameters and node correlative detection model combined,detection accuracy is effectively improved,and the calculation time of node detection is largely reduced.

Ontogenetic development in the morphology and behavior of loach(Misgurnus anguillicaudatus) during early life stages

Loach （Misgurnus anguillicaudatus） is a commercially important fish in China and an ideal aquaculture species. However, culturists experience high larval and juvenile mortality during mass production. To provide insight into ways to improve larviculture techniques, we describe the morphological characteristics and behavior of loach during the larval and early juvenile stages. Yolksae larvae ranged from 2.8 to 4.0 mm body length （BL） between days 0 to 4; preflexion larvae ranged from 3.6 to 5.5 mm BL between days 4 to 6; flexion larvae ranged from 4.8 to 8.1 mm BL between days 5 and 14; and post-flexion larvae ranged from 7. l to 15.7 mm BL between days 11 to 27; the minimum length and age of juveniles was 14.1 mm BL and 23 d, respectively. Loach are demersal from hatch through to the early juvenile stages. A suite of morphological characteristics （e.g., external gill filament and ventral mouth opening） and behavioral traits have developed to adapt to demersal living. We observed positive allometric growth in eye diameter, head length, head height, and pectoral fin length during the early larval stages, reflecting the priorities in the development of the organs essential for survival. Our results provide a basis for developing techniques to improve the survival of larval and juvenile loach during mass production.

Authentication based on feature of hand-written signature

The typical features of the coordinate and the curvature as well as the recorded time information were analyzed in the hand-written signatures.In the hand-written signature process 10 biometric features were summarized:the amount of zero speed in direction x and direction y,the amount of zero acceleration in direction x and direction y,the total time of the hand-written signatures,the total distance of the pen traveling in the hand-written process,the frequency for lifting the pen,the time for lifting the pen,the amount of the pressure higher or lower than the threshold values.The formulae of biometric features extraction were summarized.The Gauss function was used to draw the typical information from the above-mentioned biometric features,with which to establish the hidden Markov mode and to train it.The frame of double authentication was proposed by combing the signature with the digital signature.Web service technology was applied in the system to ensure the security of data transmission.The training practice indicates that the hand-written signature verification can satisfy the needs from the office automation systems.

Effective Opinion Spam Detection: A Study on Review Metadata Versus Content

Purpose:This paper aims to analyze the effectiveness of two major types of features—metadata-based(behavioral)and content-based(textual)—in opinion spam detection.Design/methodology/approach:Based on spam-detection perspectives,our approach works in three settings:review-centric(spam detection),reviewer-centric(spammer detection)and product-centric(spam-targeted product detection).Besides this,to negate any kind of classifier-bias,we employ four classifiers to get a better and unbiased reflection of the obtained results.In addition,we have proposed a new set of features which are compared against some well-known related works.The experiments performed on two real-world datasets show the effectiveness of different features in opinion spam detection.Findings:Our findings indicate that behavioral features are more efficient as well as effective than the textual to detect opinion spam across all three settings.In addition,models trained on hybrid features produce results quite similar to those trained on behavioral features than on the textual,further establishing the superiority of behavioral features as dominating indicators of opinion spam.The features used in this work provide improvement over existing features utilized in other related works.Furthermore,the computation time analysis for feature extraction phase shows the better cost efficiency of behavioral features over the textual.Research limitations:The analyses conducted in this paper are solely limited to two wellknown datasets,viz.,Yelp Zip and Yelp NYC of Yelp.com.Practical implications:The results obtained in this paper can be used to improve the detection of opinion spam,wherein the researchers may work on improving and developing feature engineering and selection techniques focused more on metadata information.Originality/value:To the best of our knowledge,this study is the first of its kind which considers three perspectives(review,reviewer and product-centric)and four classifiers to analyze the effectiveness of opinion spam detection using two major types of features.This study also introduces some novel features,which help to improve the performance of opinion spam detection methods.

Sockpuppet gang detection on social media sites

Users of social media sites can use more than one account. These identities have pseudo anonymous properties, and as such some users abuse multiple accounts to perform undesirable actions, such as posting false or misleading re- marks comments that praise or defame the work of others. The detection of multiple user accounts that are controlled by an individual or organization is important. Herein, we define the problem as sockpuppet gang （SPG） detection. First, we analyze user sentiment orientation to topics based on emo- tional phrases extracted from their posted comments. Then we evaluate the similarity between sentiment orientations of user account pairs, and build a similar-orientation network （SON） where each vertex represents a user account on a so- cial media site. In an SON, an edge exists only if the two user accounts have similar sentiment orientations to most topics. The boundary between detected SPGs may be indistinct, thus by analyzing account posting behavior features we propose a multiple random walk method to iteratively remeasure the weight of each edge. Finally, we adopt multiple community detection algorithms to detect SPGs in the network. User ac- counts in the same SPG are considered to be controlled by the same individual or organization. In our experiments on real world datasets, our method shows better performance than other contemporary methods.

Application of biomonitoring and support vector machine in water quality assessment

The behavior of schools of zebrafish （Danio rerio） was studied in acute toxicity environments. Behavioral features were extracted and a method for water quality assessment using support vector machine （SVM） was de- veloped. The behavioral parameters of fish were recorded and analyzed during one hour in an environment of a 24-h half-lethal concentration （LC50） of a pollutant. The data were used to develop a method to evaluate water quality, so as 6＋ 2＋ to give an early indication of toxicity. Four kinds of metal ions （Cu2~, Hg2~, Cr , and Cd ） were used for toxicity testing. To enhance the efficiency and accuracy of assessment, a method combining SVM and a genetic algorithm （GA） was used. The results showed that the average prediction accuracy of the method was over 80% and the time cost was acceptable. The method gave satisfactory results for a variety of metal pollutants, demonstrating that this is an effective approach to the classification of water quality.