scholarly journals Build Guide for the Geophysical Monitoring Systems Common Source Code.

2019 ◽  
Author(s):  
James Harris ◽  
James Harris ◽  
James Harris ◽  
James Harris ◽  
James Harris ◽  
...  
Keyword(s):  
Source Code ◽  
Monitoring Systems ◽  
Common Source ◽  
Geophysical Monitoring

scholarly journals Build Guide for the Geophysical Monitoring System (GMS) Common Source Code 2019 Release

2019 ◽  
Author(s):  
James M. Harris ◽  
Justin L. DuBois ◽  
Katherine Z. Rivera ◽  
David A. Manzanares
Keyword(s):  
Monitoring System ◽  
Source Code ◽  
Common Source ◽  
Geophysical Monitoring

Analysis of electromagnetic earthquake predictors based on data of magnetotelluric monitoring data (coseismic effect)

2020 ◽  
Author(s):  
Elena A. Bataleva ◽  
Keyword(s):  
Monitoring System ◽  
Test Area ◽  
Tien Shan ◽  
Monitoring Data ◽  
Electrical Anisotropy ◽  
Monitoring Systems ◽  
Geophysical Monitoring ◽  
Software Products ◽  
The North ◽  
Coseismic Effect

The article presents the results of the analysis of magnetotelluric monitoring data of the North Tien Shan using a set of methods of geophysical monitoring systems of the Bishkek geodynamic test area. The goals, objectives and structure of the magnetotelluric monitoring system are outlined. The obtained results of the study of electrical anisotropy for stationary and operational monitoring points are shown. Particular attention is paid to improving the methodology and software products.

scholarly journals Flowchart-Based Cross-Language Source Code Similarity Detection

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Feng Zhang ◽  
Guofan Li ◽  
Cong Liu ◽  
Qian Song
Keyword(s):  
Programming Languages ◽  
Programming Language ◽  
Computer Programming ◽  
Source Code ◽  
Common Source ◽  
Intermediate Structure ◽  
Plagiarism Detection ◽  
Code Obfuscation ◽  
Similarity Detection ◽  
Cross Language

Source code similarity detection has various applications in code plagiarism detection and software intellectual property protection. In computer programming teaching, students may convert the source code written in one programming language into another language for their code assignment submission. Existing similarity measures of source code written in the same language are not applicable for the cross-language code similarity detection because of syntactic differences among different programming languages. Meanwhile, existing cross-language source similarity detection approaches are susceptible to complex code obfuscation techniques, such as replacing equivalent control structure and adding redundant statements. To solve this problem, we propose a cross-language code similarity detection (CLCSD) approach based on code flowcharts. In general, two source code fragments written in different programming languages are transformed into standardized code flowcharts (SCFC), and their similarity is obtained by measuring their corresponding SCFC. More specifically, we first introduce the standardized code flowchart (SCFC) model to be the uniform flowcharts representation of source code written in different languages. SCFC is language-independent, and therefore, it can be used as the intermediate structure for source code similarity detection. Meanwhile, transformation techniques are given to transform source code written in a specific programming language into an SCFC. Second, we propose the SCFC-SPGK algorithm based on the shortest path graph kernel to measure the similarity between two SCFCs. Thus, the similarity between two pieces of source code in different programming languages is given by the similarity between SCFCs. Experimental results show that compared with existing approaches, CLCSD has higher accuracy in cross-language source code similarity detection. Furthermore, CLCSD cannot only handle common source code obfuscation techniques used by students in computer programming teaching but also obtain nearly 90% accuracy in dealing with some complex obfuscation techniques.

ULISES: An Open Source Code for Extrinsic Calibrations and Planview Generations in Coastal Video Monitoring Systems

2017 ◽  
Vol 335 ◽  
pp. 1217-1227 ◽  
Author(s):  
Gonzalo Simarro ◽  
Francesca Ribas ◽  
Amaya Álvarez ◽  
Jorge Guillén ◽  
Òscar Chic ◽  
...  
Keyword(s):  
Open Source ◽  
Source Code ◽  
Video Monitoring ◽  
Monitoring Systems ◽  
Open Source Code

Identifying groups of airborne particles by ordination

1994 ◽  
Vol 52 ◽  
pp. 856-857
Author(s):  
M. Shlepr ◽  
C. M. Vicroy
Keyword(s):  
Elemental Analysis ◽  
Energy Dispersive Spectroscopy ◽  
Manufacturing Process ◽  
Spatial Representation ◽  
Airborne Particles ◽  
Common Source ◽  
Data Set ◽  
Microelectronics Industry ◽  
Induced Changes ◽  
Source Materials

The microelectronics industry is heavily tasked with minimizing contaminates at all steps of the manufacturing process. Particles are generated by physical and/or chemical fragmentation from a mothersource. The tools and macrovolumes of chemicals used for processing, the environment surrounding the process, and the circuits themselves are all potential particle sources. A first step in eliminating these contaminants is to identify their source. Elemental analysis of the particles often proves useful toward this goal, and energy dispersive spectroscopy (EDS) is a commonly used technique. However, the large variety of source materials and process induced changes in the particles often make it difficult to discern if the particles are from a common source.Ordination is commonly used in ecology to understand community relationships. This technique usespair-wise measures of similarity. Separation of the data set is based on discrimination functions. Theend product is a spatial representation of the data with the distance between points equaling the degree of dissimilarity.

Elevated Tissue Factor and Tissue Factor Pathway Inhibitor Circulating Levels in Ischaemic Heart Disease Patients

1998 ◽  
Vol 79 (03) ◽  
pp. 495-499 ◽  
Author(s):  
Anna Maria Gori ◽  
Sandra Fedi ◽  
Ludia Chiarugi ◽  
Ignazio Simonetti ◽  
Roberto Piero Dabizzi ◽  
...  
Keyword(s):  
Heart Disease ◽  
Ischaemic Heart Disease ◽  
Tissue Factor ◽  
Plasma Levels ◽  
Tissue Factor Pathway Inhibitor ◽  
Common Source ◽  
Previous Myocardial Infarction ◽  
Control Subjects ◽  
Tissue Factor Pathway ◽  
Circulating Levels

SummarySeveral studies have shown that thrombosis and inflammation play an important role in the pathogenesis of Ischaemic Heart Disease (IHD). In particular, Tissue Factor (TF) is responsible for the thrombogenicity of the atherosclerotic plaque and plays a key role in triggering thrombin generation. The aim of this study was to evaluate the TF/Tissue Factor Pathway Inhibitor (TFPI) system in patients with IHD.We have studied 55 patients with IHD and not on heparin [18 with unstable angina (UA), 24 with effort angina (EA) and 13 with previous myocardial infarction (MI)] and 48 sex- and age-matched healthy volunteers, by measuring plasma levels of TF, TFPI, Prothrombin Fragment 1-2 (F1+2), and Thrombin Antithrombin Complexes (TAT).TF plasma levels in IHD patients (median 215.4 pg/ml; range 72.6 to 834.3 pg/ml) were significantly (p<0.001) higher than those found in control subjects (median 142.5 pg/ml; range 28.0-255.3 pg/ml).Similarly, TFPI plasma levels in IHD patients were significantly higher (median 129.0 ng/ml; range 30.3-316.8 ng/ml; p <0.001) than those found in control subjects (median 60.4 ng/ml; range 20.8-151.3 ng/ml). UA patients showed higher amounts of TF and TFPI plasma levels (TF median 255.6 pg/ml; range 148.8-834.3 pg/ml; TFPI median 137.7 ng/ml; range 38.3-316.8 ng/ml) than patients with EA (TF median 182.0 pg/ml; range 72.6-380.0 pg/ml; TFPI median 115.2 ng/ml; range 47.0-196.8 ng/ml) and MI (TF median 213.9 pg/ml; range 125.0 to 341.9 pg/ml; TFPI median 130.5 ng/ml; range 94.0-207.8 ng/ml). Similar levels of TF and TFPI were found in patients with mono- or bivasal coronary lesions. A positive correlation was observed between TF and TFPI plasma levels (r = 0.57, p <0.001). Excess thrombin formation in patients with IHD was documented by TAT (median 5.2 μg/l; range 1.7-21.0 μg/l) and F1+2 levels (median 1.4 nmol/l; range 0.6 to 6.2 nmol/l) both significantly higher (p <0.001) than those found in control subjects (TAT median 2.3 μg/l; range 1.4-4.2 μg/l; F1+2 median 0.7 nmol/l; range 0.3-1.3 nmol/l).As in other conditions associated with cell-mediated clotting activation (cancer and DIC), also in IHD high levels of circulating TF are present. Endothelial cells and monocytes are the possible common source of TF and TFPI. The blood clotting activation observed in these patients may be related to elevated TF circulating levels not sufficiently inhibited by the elevated TFPI plasma levels present.

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