Similarity Analysis Method Research Articles

Assessing the application of trace metals as paleoproxies and a chemostratigraphic tool in carbonate systems: A case study from the “Mississippian Limestone” of the midcontinent, United States

Abstract Trace metals have been successfully used to reconstruct geochemical seawater conditions of both modern and ancient environments. The majority of these efforts have been limited to marine shales, with little work on carbonate systems. Applying similar methods of trace metal analysis on carbonate-dominated rocks may provide valuable insight into the paleoseawater chemistry, such as redox state, and productivity, of ancient carbonate systems. This study evaluates the application of trace metals as paleoproxies in carbonate rocks. Middle-ramp wackestones to grainstones from the “Mississippian Limestone” in the Midcontinent were analyzed using inductively-coupled plasma mass spectrometry (ICP-MS) for both the carbonate-fraction and the bulk-fraction trace metal content. Our data show that productivity proxies, such as Cd and P, are captured within the carbonate-fraction and may reflect seawater chemistry of the system. High Cd, and moderate bionutrient (P, Ni, Zn) enrichments indicate primary productivity in the system, though it is difficult to quantify to what extent. Vanadium, Cr, U, and Mo appear to be primarily associated with the bulk-fraction content and correlate well with Al content, indicating a detrital origin. Furthermore, V, U, and Mo show no significant enrichments, and Mo/Fe ratios correlate with those of shales from a modern oxic shelf. This suggests that anoxic or euxinic conditions in the water column were not present. Trace metal content of carbonate rocks have the potential to be used in paleoenvironmental reconstruction of carbonate systems, though challenges exist such as the lack of comparable carbonate trace metal data, bias of trace metal incorporation pathways into carbonates, and diagenetic alteration.

Research on intelligent classification of multi-attribute safety information and determination of operating environment

To analyze the system safety through the field system operation data, especially the system operation information provided by the operators, and to obtain the environment scope of the safe operation of the system, the multi-attribute safety information intelligent mining method is adopted. The new definitions and methods of attribute circle classification are proposed. The improvement is using inner attribute polygon and outer attribute polygon to construct the object attribute area. Monte Carlo method is used to calculate the overlap area of the object attribute area, and define overlap and transform with similarity, which is calculated by the overlap degree and the distance between two points in multi-dimensional space. The object classification reasoning method and system adaptive environment analysis method is established. The improved similarity analysis method is more suitable for computer implementation and intelligent analysis. Based on the previous study, this paper also analyzes the similarity of operation safety evaluation given by 30 operators of an electrical system, and obtains the pair wise similarity. The results show that the algorithm classification results have good correspondence with the original data decision level, and the range of the system safe operation environment is obtained, which can lay a foundation for further study of intelligent classification and system safety operation environment optimization.

Consequence of Heat Transfer on Free Convection Flow of Casson Fluid with Hall Effect

Effect of heat transfer on free convection flow of Casson fluid over a vertical plate with Hall effect has been studied. A similarity analysis method was used to transform the system of partial differential equations describing the problem into an ordinary differential equations, Analytical solutions are obtained by solving the ODE to analyze the velocity and temperature fields. Variations of interesting parameters on the velocity, heat transfer and skin friction are observed by plotting graphs. Further, it was concluded that the Casson fluid parameter and hall parameter has an retarding influence on velocity profile and also in the skin friction.

ANALISIS METODE CHEATING PADA TES BERSKALA BESAR

This study aimed to reveal: (1) the characteristics of items of Chemistry Test in National Examination by using the classical test theory and item response theory; (2) the amount of cheating which occured by using Angoff's B-index Method, Pair 1 Method, Pair 2 Method, Modified Error Similarity Analysis (MESA) Method, and G2 Method; (3) the methods that detect more cheating in the implementation of the Chemistry Test in National Examination for high schools in the year 2011/2012 in Maluku Province. The results of the analysis with the classical test theory approach show that 77.5% items have item difficulty functioning well, 55% items have discrimination yet qualified and 70% items have distractor that works well with the index reliability test of 0,772. The analysis using the item response theory approach shows that 14 (35%) items fit with the model, the maximum function information is 11,4069 at θ = -1,6, and the magnitude of the error of measurement is 2,296. The number of pairs who are suspected of cheating is as follows: 13 pairs according to Angoff's B-index Method, 212 pairs according to Pair 1 Method, 444 pairs according to Pair 2 Method, 7 pairs according to MESA Method, and 102 pairs according to G2 Method. The most widely detecting cheating in a row is a Pair 2, Pair 1, G2, Angoff's B-index, and MESA.

Study on Typhoon Disaster Loss and Risk Prediction and Benefit Assessment of Disaster Prevention and Mitigation

ABSTRACT Based on the meteorological monitoring data of Typhoon Nesat and Haitang in Fujian Province in 2017, the data of the typhoon disaster situation and third-party evaluation of typhoon disaster prevention and mitigation benefit were investigated. By using the mean value anomaly estimation method in the similarity analysis method, it is estimated that typhoon Nesat and Haitang will cause direct economic losses of 0.8 ~ 1.8 billion yuan, and the typhoon disaster risk index evaluation model is used. The disaster risk pre-assessment of Typhoon Nesat and Haitang is carried out. The results of the study and analysis show that the pre-assessment results of Typhoon Nesat and Haitang are basically consistent with the actual disaster situation. The disaster loss was reduced to the minimum. According to the third party evaluation and investigation, it was concluded that the contribution rate of reducing economic benefits was 75% in Fujian Province for preventing and landing double typhoons Nesat and Haitang. Mainly reflected in the ship return to the port to avoid the wind, geological hazards prone to the site construction and cage culture danger removal reinforcement and other links; The contribution rate of reducing casualties is 86, mainly reflected in fishermen disembarkation and personnel transfer to safety emergency shelter and other links. The benefit of this double typhoon disaster prevention and mitigation in Fujian Province is about 0.6 billion yuan to reduce the direct economic loss. According to the investigation, the overall satisfaction of the double typhoon meteorological service is 91.0 points. Typhoon early warning information service convenience satisfaction is 91.4 points, early warning information accuracy satisfaction is 87.2 points, government departments strong anti-Taiwan disaster reduction organization, linkage departments participate in disaster prevention and mitigation high. However, there is a gap between public awareness and participation in typhoon disasters.

Data Streams Mining for Anomaly and Change Detection in Continuous Plant Operation

Data streams are collected from the real time operation of complex machines, plants and other technological systems. The collected information could be further used for different purposes, such as performance evaluation, anomaly detection, change detection or fault diagnosis of the operating systems. The analysis of the data streams is done by a calculation tool for estimating the similarity level between pairs of given chunks of data from the data stream, considered as data clouds. One of these data cloud represents a prerecorded (previously known) abnormal operation of the system, while the other data cloud represents a current (still unknown) behavior of the system. Then the similarity analysis will show how close the two data clouds are. In this paper we propose a novel method for similarity analysis that uses two types of models called Data Cloud Model (DCM) and Window Cloud Model (WCM). The DCM is obtained from the data cloud that represents a previously known operation of the system, while the WCM is obtained from the newly collected data cloud from the data stream, called window data cloud. Both data clouds have an equal length (number of data). The algorithms for creating the DCM and WCM are explained in the paper. The DCM consists of Active Grid cells that represent approximately the data density within the known data cloud. The WCM estimates the data density at the same active grid cells, but based on the data from the new window data cloud. Both densities are represented as two Histograms that are compared to each other in order to calculate the similarity level as a value between 0.0 and 1.0. Another problem discussed in the paper is finding a plausible method for detection of significant changes in the data streams. Here the moving window technique is used for collecting series of subsequent data clouds. Then two procedures of moving windows are run in parallel, each of them with different window lengths, in order to calculate the center-of-gravity of the respective data in a real time. The difference between the results of the two moving windows is used as an estimate of the change in the process operation. Both technologies developed in this paper are explain in details in the paper and illustrated on the example of real data stream from a petrochemical plant.

TRANSEXUAL “WOMANHOOD” AND THE EMERGENCE OF TRANSFEMINISM: RHETORICS OF HIV/AIDS IN THE LIGHT OF THE QUEER THEORY

ABSTRACT Objective: to analyze the female transsexual identity and the emergence of transfeminism through the context of vulnerability to HIV / AIDS in light of the Queer Theory. Method: a qualitative, descriptive, exploratory study based on the Queer Theory, carried out in a reference hospital for HIV/AIDS in the State of Pernambuco (Brazil), developed with six young transsexual women. The interviews were analyzed in the IRaMuTeQ software via the similarity analysis method. Results: the relation of male domination through the subordination of the transsexual woman, which originates in the binary, sexist and male chauvinistic heteronormative model, is emphasized. The context of vulnerability to HIV / AIDS is evidenced as a naturalized phenomenon of violence to the young "queer" woman, especially with precarious living conditions, history of family rejection, sexual violence and informal prostitution work. Lack of support from the social network and the imminent risk of transphobic violence result in damage to their physical and mental integrity. The following categories were observed: Emergence of transfeminism through symbolic violence and female transsexual identity and the context of vulnerability. Conclusion: the social impacts of the minimal state policy, social class cohesion (binarism, sexism, racism and male chauvinism), unequal social capital and culture of abjection of transsexual women reflect the context of the HIV / AIDS epidemic and inequalities that result in individual, contextual and programmatic vulnerability and in factors which limit the attainment of a healthy life.

Staged Method of Code Similarity Analysis for Firmware Vulnerability Detection

The security situation of the Internet of Things (IoT) is more serious than ever, and there is an urgent need to detect and patch device vulnerability rapidly. With the astronomical numbers of IoT devices, it is very difficult to execute regular security inspections. Existing vulnerability detection technology based on simple feature matching cannot reach high accuracy to detect firmware vulnerabilities while using a control flow graph matching directly has proven to be too expensive. To address the problem of accurate and efficient, we present a method of staged firmware vulnerability detection based on code similarity. The first stage, function embedding based on neural network is used to analyze the similarities among functions, and large-scale firmware security inspection can be achieved efficiently. The second stage, the similarity among function local call flow graphs is calculated for fine-grained firmware security analysis, and this stage can improve the accuracy of vulnerability detection. We compared our method with state-of-the-art approaches, and the experimental results demonstrate that our method is more accurate. The average retraining time of our method is 1 h, and the real-world firmware vulnerability detection experiment of our method demonstrates that the true positive rate of the top 30 is as high as 86%.

New Experimental Limits on Exotic Spin-Spin-Velocity-Dependent Interactions by Using SmCo_{5} Spin Sources.

Despite the great success of the standard model (SM), there still exist mysteries like the nature of dark matter, the strong CP problems, etc. To solve them, many theories proposed new bosons beyond the SM that can mediate new forces. Here, we report the latest results of searching for possible new long-range spin-spin-velocity-dependent forces (SSVDFs), based on specially designed iron-shielded SmCo_{5} spin sources and a spin exchange relaxation free comagnetometer. With help from the similarity analysis method, new constraints on some forms of SSVDFs between electrons have been obtained, which represent up to more than 11 orders of magnitude tighter limits than previous experiments for the force range of 5cm-1km.

The accuracy of the cheating detection methods in large-scale tests: mathematics national examination

This study aimed to describe (1) the characteristics of items based on the Item Response Theory, (2) the cheating level in the implementation of the national examinartion based on Angoffs B-Index method, Pair 1 method, Pair 2 method, Modified Error Similarity Analysis (MESA) method, and G2 method, (3) the most accurate method to detect the cheating in the mathematics national examination at the senior secondary school level in the academic year of 2015/2016 in East Nusa Tenggara Province. The result of the item response theory analysis showed that 17 (42.5%) items of the mathematics national examination fit with the 3-PL model, with the maximum information function of 58.0128 at 1.6, and the measurement error of 0.1313. The number of pairs detected to be cheating by Angoff’s B-Index method was 63 pairs, that by the Pair 1 method was 52 pairs, that by the Pair 2 method was 141 pairs, that by MESA method was 67 pairs, and that by the G2 method was 183 pairs. The methods which could detect most pairs doing cheating were the G2 method, the Pair 2 method, the MESA method, Angoff’s B-Index method, and the Pair 1 method successively. The methods which could accurately detect cheating based on the computation of the standard error were Angoff’s B-Index method, the G2 method, the MESA method, the Pair 1 method, and the Pair 2 method successively.

Super-dimension-based three-dimensional nonstationary porous medium reconstruction from single two-dimensional image

Nonstationary porous media widely occur in nature, and a means of determining their three-dimensional (3D) structure can be highly significant for various objectives, such as seepage analysis and physical property investigation. The super-dimension (SD) reconstruction concept can be used to reconstruct the 3D structure of a stationary porous medium from a single two-dimensional (2D) reference image, which is referred to as the training image. In this study, we applied the SD reconstruction concept to a nonstationary porous medium. By introducing a probability-based image block selection mechanism into the SD reconstruction method, we established the statistical relationship between the number of “0” pixels (grain phase) in the 2D and 3D image blocks of the real core stored in the SD dictionary. We employed this identified relationship for the selection of dictionary elements to maintain the nonstationary information of the core during the reconstruction. In addition, we proposed relevant strategies for "block matching", "template selection", "element storage" and "pixel filling" in the reconstruction process. On this basis, we developed an original nonstationary medium reconstruction algorithm. To verify the accuracy of reconstruction results through experiments, we employed statistical similarity analysis methods based on the two-point correlation function and morphological similarity analysis tools for determining the pore radius and pore volume distributions. Finally, the seepage characteristics were analyzed. The proposed method was confirmed to be effective for reconstructing a nonstationary structure with statistical and morphological features consistent with the samples. The universality of the algorithm proposed here was also verified by its successful application to the stationary porous medium.

Zur Semantik der Tages- und Epochenbezeichnungen im Deutschen und Polnischen

The article refers to Ph.D. dissertation in which the author investigated adverbials of time in German and Polish. Using a similar method of analysis, namely establishing the relations occurring between the point of time of the event, the reference and the speech act, the author describes the usage of definitions of a day and longer periods (epochs). The author refers at the same time to simple formulas of formal logic and juxtaposes all the relevant expressions in both languages.

A P waves’ automatic picking by detecting the changes of seismic signals’ stationary random process through similarity analysis

Detection of P waves in the seismic phrases is essential for studying the earthquake ground motion. A P waves’ automatic picking by detecting the changes of seismic signals’ stationary random process is put forward through the similarity analysis in the paper. Firstly a known template of stationary random process is composed or chosen. And the similarity coefficients between the chosen template and the seismic signal are calculated, and they present the stability of the seismic signal. Then a given threshold of the similarity coefficients’ changes is applied to detect the significant changes of the similarity coefficients, which represents the P waves’ arrivals. By the similarity coefficient method, the detecting results of P waves accompanied confidence level are obtained. This similarity analysis method was tested by both the main-shocks and the aftershocks of the Ms 8.0 Wenchuan Earthquake. Comparing the picking results, the similarity coefficient method performed better than the short-term and long-term average ratio (STA/LTA) algorithm. For given examples, the mean error of the P waves’ picking time by the similarity analysis method (−0.13 s) is less than the mean error by the STA/LTA method (−0.33 s). When noise is added, the mean error of the P waves’ picking time by the similarity analysis method increased to 0.25 s, that is still less than the mean error by the STA/LTA method (4.07 s).

Automated high-performance liquid chromatography with diode-array detection and gas chromatography with flame ionization detection technique to identify Chinese pomelos with protected geographical indication

Pomelos are one of the most popular mid-autumn fruits in China. Given the high price of Guangxi Rongxian Sha Tin and Fujian Guanxi Honey pomelos, some inferior-quality Guangxi Rongxian Sha Tin and Fujian Guanxi Honey pomelos appear on the market from other planting areas, thereby damaging the interests of customers and fruit growers. To differentiate the two Chinese pomelos with protected geographical indication (PGI), namely, Guangxi Rongxian Sha Tin and Fujian Guanxi Honey pomelos, from those produced in 14 other areas of China, we combine the chromatographic techniques of high-performance liquid chromatography with diode-array detection and gas chromatography with flame ionization detection with the chemometric methods of similarity analysis (SA), principal component analysis (PCA), and cluster analysis (CA). The results of SA reveal that the fingerprint chromatograms of exocarpium from different cultivars diverge and that the correlation coefficient is less than 0.3. However, the same species from other regions give highly similar results, with correlation coefficient is greater than 0.85. In addition, PCA and CA successfully differentiate these two PGI Chinese pomelos from 14 other pomelo samples in terms of chemical composition and concentrations. In summary, the combination of chromatographic chemical fingerprints with appropriate chemometric methods provides a simple and rapid method to evaluate the quality and origin of PGI Chinese pomelos.

User Profile Based Paper Recommendation System

As the spread of science and the number of researchers working in academic fields increase, there is also a considerable increase in the number of academic studies. Researchers always follow new works published for keeping their knowledge up to date. However, with many different sources and thousands of academic publications published every day, academics are not always able to find publications about their subjects. Today, almost all of online academic databases employ a recommendation module which only considers the studies similar to the paper that the user looked at. However, a recommendation system based on the information of a single article is often not enough. In this study, the proposed method recommends by considering user's publications, user’s co-authors and co-authors’ papers. Therefore, meta-data of the articles published by the researcher in the past are scored on a time-base basis with the method we propose. With the help of the sum of scores, there is a score of the user profile in the subject matter. It aims to find the closest studies to the profile of the user by searching with the method propsoed in the data pool which we created from the exact contents of hundreds of thousands of academic works. In the proposed method, TF-IDF is used from frequency-based similarity analysis methods. In the evaluation phase, the performance of the proposed method was examined. The success test of the method was measured by several different methods. These are to be evaluated by presenting them to real users and the other is to compare with existing data. The results are very promising and demonstrate that the method can produce accurate and quality results.

SCANNET: A Software for Identification of Communities in Networks

The Network Similarity Analysis (NSA) method is a powerful tool for identifying communities in weighted networks whose links depend on the edge's weight. The method can be applied to different types of networks, with nodes and edges receiving different interpretations. For instance, the network can be constructed based on the similarity between proteins that play the same function in distinct species, or between DNA sequences that share the same evolutionary origins. The original implementation of the NSA method consisted of pipeline of algorithms implemented in different programming languages, making it difficult to use. SCANNET is a software developed to integrate all algorithms of the NSA method into a unique computational environment, to perform all the steps needed to identify communities in networks in a single workflow. As an example for this paper, we applied SCANNET to implement a single workflow that correctly identified the communities in protein networks containing different sequences of enzymes. SCANNET has correctly generated the protein networks, the dendrograms, and the color representation of the neighborhood matrix for each of the studied case. The experiments have shown that SCANNET is a simple, efficient, reliable, and intuitive implementation of the NSA method for identifies communities in the case of protein networks. In addition, SCANNET generates charts with all the distances between pairs of neighboring networks, which facilitate the identification of critical networks for community identification. SCANNET also generates dendrograms, and color representations of neighborhood matrices that can be manipulated by different analytical software, such as Origin Lab or Matlab, offering a friendly interface.

SSAW: A new sequence similarity analysis method based on the stationary discrete wavelet transform

BackgroundAlignment-free sequence similarity analysis methods often lead to significant savings in computational time over alignment-based counterparts.ResultsA new alignment-free sequence similarity analysis method, called SSAW is proposed. SSAW stands for Sequence Similarity Analysis using the Stationary Discrete Wavelet Transform (SDWT). It extracts k-mers from a sequence, then maps each k-mer to a complex number field. Then, the series of complex numbers formed are transformed into feature vectors using the stationary discrete wavelet transform. After these steps, the original sequence is turned into a feature vector with numeric values, which can then be used for clustering and/or classification.ConclusionsUsing two different types of applications, namely, clustering and classification, we compared SSAW against the the-state-of-the-art alignment free sequence analysis methods. SSAW demonstrates competitive or superior performance in terms of standard indicators, such as accuracy, F-score, precision, and recall. The running time was significantly better in most cases. These make SSAW a suitable method for sequence analysis, especially, given the rapidly increasing volumes of sequence data required by most modern applications.

Research on clustering analysis of Internet public opinion

In order to enhance the real-time performance of Internet public opinion recognizing and early warning, and improve the accuracy of the analysis of Internet public opinion for hot spots, similarity analysis methods of Internet public opinion are put forward. Firstly, web crawler technology is introduced for obtaining accurate and comprehensive public opinion. Secondly, propose similarity algorithms from the aspects of known and unknown of the subject. At the same time, considering the uncertainty and fuzzy of Internet public opinion, the concept of information entropy is introduced, and present a similarity analysis approach of Internet public opinion based on information entropy, and can cluster and identify hot spots and crisis events of Internet public opinion. Experimental results show that the proposed methods can quickly obtain the Internet public opinion, and has high accuracy rate of clustering, which provide an important technical support for Internet public opinion monitoring and recognizing.

Application of similarity analysis to flood forecasting

[Objective] There are plenty of useful information in hydrological observations. Predicting future flood on the basis of similarity information in historical records is an effective and promising approach. [Method] In this paper, a multi-measure similarity analysis method of rainstorms is developed based on “quantity”, “type” similarity indicators, the earth mover’s distance (EMD) and the rainstorm distribution similarity indicator. Search the similar rainstorm and its corresponding typical flood in historical library and then scale the typical flood process according to the ratio of rainfall amounts to achieve flood forecasting. [Result] The method is applied to a case study in Xinmiao station of Kuye River. The results show that with the accelerating information of rainstorm and flood process, the forecasted flood process is updated continuously, and the prediction accuracy is gradually increasing. [Conclusion] The proposed similarity analysis method is effective and applicable to flood forecasting.

Methods of structural analysis of business processes

The article outlines selected methods for analyzing business processes: their definitions and instances. The methods for analytical processing of processes constitute a component of the Business Intelligence environment - process warehouses, including methods for analytical processing and exploration of the collected process definitions and instances - i.e. process mining. One of the main elements of the analysis of processes is to determine the similarity between them. In systems for analyzing large sets of elements, the method of determining similarity should be efficiency because is the basis for others analysis methods, e.g. clustering, classification, etc. A method for analyzing structural similarity of business processes, based on the similarity of sequences of genetic tags of such processes, was presented using the similarity analysis methods based on the editing distance and the developed methods of structural similarity: GNM, DNM, GCM, DCM. The presented similarity methods were used to clustering processes and to determine the central element of the cluster. The developed methods form the basis for the development of similarity methods extended to aspects of semantic similarity of business processes and methods of analysis and exploration of processes.