scholarly journals A discharge estimation model using a spatial correlation method in Cipanunjang-Cileunca Reservoir, Bandung District, West Java Province

2021 ◽  
Vol 716 (1) ◽  
pp. 012005
Author(s):  
Mariana Marselina ◽  
Arwin Sabar
Keyword(s):  
Spatial Correlation ◽  
Correlation Method ◽  
Estimation Model ◽  
West Java ◽  
Discharge Estimation

scholarly journals Cell Matrix Remodeling Ability Shown by Image Spatial Correlation

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Chi-Li Chiu ◽  
Michelle A. Digman ◽  
Enrico Gratton
Keyword(s):  
Cell Line ◽  
Spatial Correlation ◽  
Cell Lines ◽  
Second Harmonic ◽  
Correlation Method ◽  
Fibroblast Cell ◽  
Breast Cancer Cell Lines ◽  
Matrix Remodeling ◽  
Ecm Remodeling ◽  
Collagen Remodeling

Extracellular matrix (ECM) remodeling is a critical step of many biological and pathological processes. However, most of the studies to date lack a quantitative method to measure ECM remodeling at a scale comparable to cell size. Here, we applied image spatial correlation to collagen second harmonic generation (SHG) images to quantitatively evaluate the degree of collagen remodeling by cells. We propose a simple statistical method based on spatial correlation functions to determine the size of high collagen density area around cells. We applied our method to measure collagen remodeling by two breast cancer cell lines (MDA-MB-231 and MCF-7), which display different degrees of invasiveness, and a fibroblast cell line (NIH/3T3). We found distinct collagen compaction levels of these three cell lines by applying the spatial correlation method, indicating different collagen remodeling ability. Furthermore, we quantitatively measured the effect of Latrunculin B and Marimastat on MDA-MB-231 cell line collagen remodeling ability and showed that significant collagen compaction level decreases with these treatments.

scholarly journals Characteristics of saw-tooth bars on the ebb-tidal deltas of the Wadden Islands

2019 ◽  
Vol 69 (11-12) ◽  
pp. 1273-1285 ◽  
Author(s):  
Laura Brakenhoff ◽  
Gerben Ruessink ◽  
Maarten van der Vegt
Keyword(s):  
Sediment Transport ◽  
Spatial Correlation ◽  
Formation Mechanism ◽  
Wave Length ◽  
Correlation Method ◽  
Migration Speed ◽  
Transport Pathways ◽  
Migration Rates ◽  
Sand Bars ◽  
And Migration

Abstract Saw-tooth bars are shore-oblique sand bars that are found along most ebb-tidal deltas of the Frisian Wadden Islands. Although they might significantly affect sediment transport pathways and volumes on the deltas, their general characteristics and dynamics are largely unknown. The main aim of this paper is to determine the typical saw-tooth bar heights, wave lengths, widths, orientations, migration rates and depths of occurrence. To this end, we analysed bathymetries from the Dutch and German Frisian Wadden Islands between 1970 and 2015. Bar heights range between 0.5 and 2 m, and wave lengths range between 300 and 900 m, with an average of 670 m, and the bar crests have a down contour orientation of approximately 25°. The bars are between 800 and 2200 m wide. Saw-tooth bars are located at depths from 3 to 12 m, depending on the slope of the area. Migration speeds of up to 112 m/year were found, using a spatial correlation method. Bar height and migration speed are positively linearly correlated, as well as bar wave length and bar width, bar height and the orientation of the depth contours and migration speed and the orientation of the area. The derived characteristics are compared to those of other bar types to evaluate hypotheses regarding the formation mechanism of the bars.

scholarly journals Developing a Discharge Estimation Model for Ungauged Watershed Using CNN and Hydrological Image

Water ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 3534
Author(s):  
Da Ye Kim ◽  
Chul Min Song
Keyword(s):  
Model Development ◽  
Global Scale ◽  
Continuous Variable ◽  
Estimation Model ◽  
Discharge Simulation ◽  
Ungauged Watershed ◽  
Model Training ◽  
Discharge Estimation ◽  
Fully Connected ◽  
Ungauged Watersheds

This study aimed to estimate the discharge in ungauged watersheds. To this end, we herein deviated from the model development methodology of previous studies and used convolution neural network (CNN), a deep training algorithm, and hydrological images. As the CNN model was developed for solving classification issues in general, it is unsuitable for simulating the discharge, which is a continuous variable. Therefore, the fully connected layer of the CNN model was improved. Moreover, images reflecting the hydrological conditions rather than a general photograph were used as input data for the CNN model. Three study areas that have discharge gauged data were set for the model’s training and testing. The data from two of the three study areas were used for CNN model training, and the data of the other were used to evaluate model prediction performance. The results of this study demonstrate a moderate predictive success of the discharge of an ungauged watershed using the CNN model and hydrological images. Therefore, it can be suitable as a methodology for the discharge estimation of ungauged watersheds. Simultaneously, it is expected that our methodology can be applied to the field of remote sensing or to the field of real-time discharge simulation using satellite imagery on a global scale or across a wide area.

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