scholarly journals Lobster (Panulirus argus) captures and their relation with environmental variables obtained by orbital sensors for Cuban waters (1997-2005)

2008 ◽  
Vol 56 (3) ◽  
pp. 225-237 ◽  
Author(s):  
Regla Duthit Somoza ◽  
Milton Kampel ◽  
Frederico de Moraes Rudorff ◽  
Ronald B. Sousa ◽  
Susana Cobas
Keyword(s):  
Environmental Variables ◽  
Cross Correlation ◽  
Correlation Coefficients ◽  
Panulirus Argus ◽  
Spatial And Temporal Variability ◽  
Wide Field ◽  
Chl A ◽  
Cross Correlation Analysis ◽  
The Cross ◽  
Almost All

Chlorophyll concentrations (Chl a) data obtained from the Sea Viewing Wide Field of View Sensor (SeaWIFS) ocean color monthly images, Sea Surface Temperature (SST) pathfinder data obtained from the Advanced Very High Resolution Radiometer (AVHRR) sensors, and lobster (Panulirus argus) captures at the Cuban shelf were examined in order to analyze their spatial and temporal variability. A cross-correlation analysis was made between the standardized anomalies of the environmental variables (Chl a and SST) and the standardized anomalies of lobster captures for each fishery zones for the period between 1997 and 2005. For the deep waters adjacent to the fishing zones it was not observed a clear Chl a seasonality and on average the lowest values occurred south of the Island. It is with the three years lag that Chl a had the greatest numbers of significant correlation coefficients for almost all fishing zones. However, the cross-correlation coefficients with SST showed higher values with 1,5 year lag at all zones. Since the two environmental variables obtained by satellite sensors (SST and Chl a) influence the lobsters mainly during the planktonic life cycle, the cross-correlation with lobster captures begin to show significant indexes with lags of 1.5 years or more.

scholarly journals DrumCorr program for selecting volcanic earthquake multiplets based on cross-correlation analysis

2021 ◽  
Vol 2094 (3) ◽  
pp. 032048
Author(s):  
I A Zavedevkin ◽  
A A Shakirova ◽  
P P Firstov
Keyword(s):  
Seismic Data ◽  
Cross Correlation ◽  
Correlation Coefficients ◽  
Volcanic Earthquake ◽  
Primary Analysis ◽  
Volcanic Earthquakes ◽  
Cross Correlation Analysis ◽  
Wave Forms ◽  
The Cross ◽  
Correlation Detector

Abstract The DrumCorr program based on cross-correlation detection has been developed to identify multiplets of the volcanic earthquakes. The program is implemented in Python 3 and reads ASCII and MiniSEED seismic data formats. The article presents the algorithm of the program, describing the cross-correlation detector and an example of subsequent processing of seismic data. The program was applied to volcanic earthquakes of the «drumbeats» seismic regime and allowed to identify earthquake multiplets characterized by various wave forms. The article presents the algorithm of the program, describing the cross-correlation detector, the features of the weak volcanic earthquakes selection by the STA/LTA method. And the primary analysis of the values of the correlation coefficients with the calculation of their standard errors depending on different signal-to-noise ratios.

scholarly journals Insights to enhance the examination of tool marks in human cartilage

2021 ◽  
Author(s):  
Matthias Weber ◽  
Anja Niehoff ◽  
Markus A. Rothschild
Keyword(s):  
Light Microscope ◽  
Cross Correlation ◽  
Costal Cartilage ◽  
Correlation Coefficients ◽  
Human Cartilage ◽  
Cut Marks ◽  
Tool Marks ◽  
The Cross ◽  
Cleaning Methods ◽  
The Individual

AbstractThis work deals with the examination of tool marks in human cartilage. We compared the effectiveness of several cleaning methods on cut marks in porcine cartilage. The method cleaning by multiple casts achieved the significantly highest scores (P = 0.02). Furthermore, we examined the grain-like elevations (dots) located on casts of cut cartilage. The results of this study suggest that the casting material forms these dots when penetrating cartilage cavities, which are areas where the strong collagen fibres leave space for the chondrocytes. We performed fixation experiments to avoid this, without success. In addition, 31 casting materials were compared regarding contrast under light-microscope and 3D tool marks scanner. Under the light-microscope, brown materials achieved significantly higher values than grey (P = 0.02) or black (P = 0.00) whereas under the 3D scanner, black materials reached higher contrast values than grey (P = 0.04) or brown (P = 0.047). To compare the accuracy and reproducibility of 6 test materials for cartilage, we used 10 knives to create cut marks that were subsequently scanned. During the alignment of the individual signals of each mark, the cross-correlation coefficients (Xmax) and lags (LXmax) were calculated. The signals of the marks in agarose were aligned with significantly fewer lags and achieved significantly higher cross-correlation coefficients compared to all tested materials (both P = 0.00). Moreover, we determined the cross-correlation coefficients (XC) for known-matches (KM) per material. Agarose achieved significantly higher values than AccuTrans®, Clear Ballistics™, and gelatine (all P = 0.00). The results of this work provide valuable insights for the forensic investigation of marks in human costal cartilage.

scholarly journals Advancements of the examination of tool marks in human cartilage

2021 ◽  
Author(s):  
Matthias Weber ◽  
Anja Niehoff ◽  
Markus A. Rothschild
Keyword(s):  
Light Microscope ◽  
Cross Correlation ◽  
Costal Cartilage ◽  
Correlation Coefficients ◽  
Human Cartilage ◽  
Cut Marks ◽  
Tool Marks ◽  
The Cross ◽  
Cleaning Methods ◽  
The Individual

Abstract This work deals with the examination of tool marks in human cartilage. We compared the effectiveness of several cleaning methods on cut marks in porcine cartilage. The method cleaning by multiple casts achieved the significantly highest scores (P = 0.02). Furthermore, we examined the grain-like elevations (dots) located on casts of cut cartilage. The results of this study suggest that the casting material forms these dots when penetrating cartilage cavities, which are areas where the strong collagen fibers leave space for the chondrocytes. We performed fixation experiments to avoid this, without success. In addition, 31 casting materials were compared regarding contrast under light-microscope and 3D tool marks scanner. Under the light-microscope, brown materials achieved significantly higher values than grey (P = 0.02) or black (P = 0.00) whereas under the 3D scanner, black materials reached higher contrast values than grey (P = 0.04) or brown (P = 0.047). To compare the accuracy and reproducibility of 6 test materials for cartilage, we used 10 knives to create cut marks that were subsequently scanned. During the alignment of the individual signals of each mark, the cross-correlation coefficients (Xmax) and lags (LXmax) were calculated. The signals of the marks in agarose were aligned with significantly fewer lags and achieved significantly higher cross-correlation coefficients compared to all tested materials (both P = 0.00). Moreover, we determined the cross-correlation coefficients (XC) for known-matches (KM) per material. Agarose achieved significantly higher values than AccuTrans®, Clear Ballistics™, and gelatine (all P = 0.00). The results of this work provide valuable insights for the forensic investigation of marks in human costal cartilage.

Analyzing the Impact of COVID-19 on the Cross-Correlations between Financial Search Engine Data and Movie Box Office

2020 ◽  
pp. 2150021
Author(s):  
Renyu Wang ◽  
Yujie Xie ◽  
Hong Chen ◽  
Guozhu Jia
Keyword(s):  
Financial Market ◽  
Cross Correlation ◽  
Multifractal Spectrum ◽  
Movie Industry ◽  
Box Office ◽  
Cross Correlation Analysis ◽  
Cross Correlations ◽  
The Cross ◽  
Google Search ◽  
The Impact

This paper explores the COVID-19 influences on the cross-correlation between the movie market and the financial market. The nonlinear cross-correlations between movie box office data and Google search volumes of financial terms such as Dow Jones Industrial Average (DJIA), NASDAQ and PMI are investigated based on multifractal detrended cross-correlation analysis (MF-DCCA). The empirical results show there are nonlinear cross-correlations between movie market and financial market. Metrics such as Hurst exponents, singular exponents and multifractal spectrum demonstrate that the cross-correlation between movie market and financial market is persistent, and the cross-correlation in long term is more stable than that in short term. In the COVID-19 period, the multifractal features of cross-correlation become stronger implying that COVID-19 enhanced the complexity between the movie industry and the financial market. Furthermore, through the rolling window analysis, the Hurst exponent dynamic trends indicate that COVID-19 has a clear influence on the cross-correlation between movie market and financial market.

Multiscale Multifractal Detrended Cross-Correlation Analysis of High-Frequency Financial Time Series

2019 ◽  
Vol 18 (03) ◽  
pp. 1950014 ◽  
Author(s):  
Jingjing Huang ◽  
Danlei Gu
Keyword(s):  
Time Series ◽  
Correlation Analysis ◽  
High Frequency ◽  
Cross Correlation ◽  
Financial Time Series ◽  
Chinese Market ◽  
Change Of Scale ◽  
Cross Correlation Analysis ◽  
Cross Correlations ◽  
The Cross

In order to obtain richer information on the cross-correlation properties between two time series, we introduce a method called multiscale multifractal detrended cross-correlation analysis (MM-DCCA). This method is based on the Hurst surface and can be used to study the non-linear relationship between two time series. By sweeping through all the scale ranges of the multifractal structure of the complex system, it can present more information than the multifractal detrended cross-correlation analysis (MF-DCCA). In this paper, we use the MM-DCCA method to study the cross-correlations between two sets of artificial data and two sets of 5[Formula: see text]min high-frequency stock data from home and abroad. They are SZSE and SSEC in the Chinese market, and DJI and NASDAQ in the US market. We use Hurst surface and Hurst exponential distribution histogram to analyze the research objects and find that SSEC, SZSE and DJI, NASDAQ all show multifractal properties and long-range cross-correlations. We find that the fluctuation of the Hurst surface is related to the positive and negative of [Formula: see text], the change of scale range, the difference of national system, and the length of time series. The results show that the MM-DCCA method can give more abundant information and more detailed dynamic processes.

AN ANALOG SEISMlC CORRELATOR

Geophysics ◽  
1961 ◽  
Vol 26 (3) ◽  
pp. 298-308 ◽  
Author(s):  
F. N. Tullos ◽  
L. C. Cummings
Keyword(s):  
Seismic Data ◽  
Cross Correlation ◽  
Correlation Coefficients ◽  
Analog Computer ◽  
Signal Power ◽  
Arrival Times ◽  
Data Points ◽  
The Cross ◽  
Correlation Process ◽  
Total Average

An analog computer has been built to compute the cross‐correlation coefficients of multi‐trace seismograms. The evaluation program has shown that the computer has greater accuracy than is normally required to compute the cross‐correlation functions of short samples of data. Points on the correlation curves are computed and plotted at the rate of approximately 50 points per minute. Scanning is in difference of arrival times (Δt) across the record, with increments of [Formula: see text] to 16 millisecond. The correlation process is completely automatic with the exception of normalization, which is approximated by holding the total average signal power constant with a ganged attenuator. Analysis of synthetic and actual seismic data indicates that the correlation will be an interpretational aid in areas where the data are poor.

scholarly journals Delineation of marine ecosystem zones in the northern Arabian Sea during winter

2018 ◽  
Vol 15 (5) ◽  
pp. 1395-1414 ◽  
Author(s):  
Saleem Shalin ◽  
Annette Samuelsen ◽  
Anton Korosov ◽  
Nandini Menon ◽  
Björn C. Backeberg ◽  
...  
Keyword(s):  
Mixed Layer ◽  
Temporal Variability ◽  
Arabian Sea ◽  
Mixed Layer Depth ◽  
Marine Ecosystem ◽  
Chlorophyll Concentration ◽  
Spatial And Temporal Variability ◽  
Chl A ◽  
Northern Arabian Sea ◽  
Cross Correlation Analysis

Abstract. The spatial and temporal variability of marine autotrophic abundance, expressed as chlorophyll concentration, is monitored from space and used to delineate the surface signature of marine ecosystem zones with distinct optical characteristics. An objective zoning method is presented and applied to satellite-derived Chlorophyll a (Chl a) data from the northern Arabian Sea (50–75∘ E and 15–30∘ N) during the winter months (November–March). Principal component analysis (PCA) and cluster analysis (CA) were used to statistically delineate the Chl a into zones with similar surface distribution patterns and temporal variability. The PCA identifies principal components of variability and the CA splits these into zones based on similar characteristics. Based on the temporal variability of the Chl a pattern within the study area, the statistical clustering revealed six distinct ecological zones. The obtained zones are related to the Longhurst provinces to evaluate how these compared to established ecological provinces. The Chl a variability within each zone was then compared with the variability of oceanic and atmospheric properties viz. mixed-layer depth (MLD), wind speed, sea-surface temperature (SST), photosynthetically active radiation (PAR), nitrate and dust optical thickness (DOT) as an indication of atmospheric input of iron to the ocean. The analysis showed that in all zones, peak values of Chl a coincided with low SST and deep MLD. The rate of decrease in SST and the deepening of MLD are observed to trigger the algae bloom events in the first four zones. Lagged cross-correlation analysis shows that peak Chl a follows peak MLD and SST minima. The MLD time lag is shorter than the SST lag by 8 days, indicating that the cool surface conditions might have enhanced mixing, leading to increased primary production in the study area. An analysis of monthly climatological nitrate values showed increased concentrations associated with the deepening of the mixed layer. The input of iron seems to be important in both the open-ocean and coastal areas of the northern and north-western parts of the northern Arabian Sea, where the seasonal variability of the Chl a pattern closely follows the variability of iron deposition.

Research on Feature Recognition of Nuclear Power Equipment Based on the Optimal Wavelet Basis

2013 ◽  
Author(s):  
Bin Li ◽  
Hong Xia
Keyword(s):  
Nuclear Power ◽  
Cross Correlation ◽  
Feature Recognition ◽  
Correlation Coefficients ◽  
Frequency Component ◽  
Discrete Wavelet ◽  
Wavelet Basis ◽  
Cracked Rotor ◽  
Coolant Pump ◽  
The Cross

Touching upon that the crack fault of the rotor may occur after the reactor coolant pump (RCP) has operated a long time, the fault feature can be identified effectively by the method of the wavelet analysis. In this research, based on the simulation signal of crack fault and the method of discrete wavelet transform (DWT), the cross-correlation coefficients between the fault signal and the different wavelet basis which are selected from the wavelet basis library can be computed. After confirming the maximum of the cross-correlation coefficients, the optimal wavelet basis applied to the fault signal of the cracked rotor will be found. And the main frequency component of the fault feature is recognized by use of the wavelet packet transform (WPT) based on the optimal wavelet basis. The results of simulation illustrate that the wavelet basis selected by the maximum cross-correlation coefficients can become the optimal wavelet basis, and the fault feature of the cracked rotor can be recognized effectively.

ON THE APPLICATION OF THE CROSS-CORRELATIONS IN THE CHINESE FUND MARKET: DESCRIPTIVE PROPERTIES AND SCALING BEHAVIORS

2011 ◽  
Vol 14 (01) ◽  
pp. 97-109
Author(s):  
WEIBING DENG ◽  
WEI LI ◽  
XU CAI ◽  
QIUPING A. WANG
Keyword(s):  
Correlation Matrix ◽  
Cross Correlation ◽  
Matrix Theory ◽  
Factorial Moment ◽  
Correlation Coefficients ◽  
Mean Value ◽  
Distribution Of Eigenvalues ◽  
Cross Correlations ◽  
The Cross ◽  
Simple Definition

On the basis of the relative daily logarithmic returns of 88 different funds in the Chinese fund market (CFM) from June 2005 to October 2009, we construct the cross-correlation matrix of the CFM. It is shown that the logarithmic returns follow an exponential distribution, which is commonly shared by some emerging markets. We hereby analyze the statistical properties of the cross-correlation coefficients in different time periods, such as the distribution, the mean value, the standard deviation, the skewness and the kurtosis. By using the method of the scaled factorial moment, we observe the intermittence phenomenon in the distribution of the cross-correlation coefficients. Also by employing the random matrix theory (RMT), we find a few isolated large eigenvalues of the cross-correlation matrix, and the distribution of eigenvalues exhibits the power-law tails. Furthermore, we study the features of the correlation strength with a simple definition.

MULTISCALE DETRENDED CROSS-CORRELATION ANALYSIS OF STOCK MARKETS

Fractals ◽  
2014 ◽  
Vol 22 (04) ◽  
pp. 1450007 ◽  
Author(s):  
YI YIN ◽  
PENGJIAN SHANG
Keyword(s):  
Correlation Analysis ◽  
Stock Markets ◽  
Cross Correlation ◽  
Statistical Characteristics ◽  
Detailed Knowledge ◽  
Stock Indices ◽  
Regional Markets ◽  
Cross Correlation Analysis ◽  
Cross Correlations ◽  
The Cross

In this paper, we employ the detrended cross-correlation analysis (DCCA) to investigate the cross-correlations between different stock markets. We report the results of cross-correlated behaviors in US, Chinese and European stock markets in period 1997–2012 by using DCCA method. The DCCA shows the cross-correlated behaviors of intra-regional and inter-regional stock markets in the short and long term which display the similarities and differences of cross-correlated behaviors simply and roughly and the persistence of cross-correlated behaviors of fluctuations. Then, because of the limitation and inapplicability of DCCA method, we propose multiscale detrended cross-correlation analysis (MSDCCA) method to avoid "a priori" selecting the ranges of scales over which two coefficients of the classical DCCA method are identified, and employ MSDCCA to reanalyze these cross-correlations to exhibit some important details such as the existence and position of minimum, maximum and bimodal distribution which are lost if the scale structure is described by two coefficients only and essential differences and similarities in the scale structures of cross-correlation of intra-regional and inter-regional markets. More statistical characteristics of cross-correlation obtained by MSDCCA method help us to understand how two different stock markets influence each other and to analyze the influence from thus two inter-regional markets on the cross-correlation in detail, thus we get a richer and more detailed knowledge of the complex evolutions of dynamics of the cross-correlations between stock markets. The application of MSDCCA is important to promote our understanding of the internal mechanisms and structures of financial markets and helps to forecast the stock indices based on our current results demonstrated the cross-correlations between stock indices. We also discuss the MSDCCA methods of secant rolling window with different sizes and, lastly, provide some relevant implications and issue.

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