ANN model for predicting the impact of submerged aquatic weeds existence on the hydraulic performance of branched open channel system accompanied by water structures

2007 ◽  
Vol 21 (7) ◽  
pp. 1139-1149 ◽  
Author(s):  
Alla E. Abdin ◽  
Mostafa A. M. Abdeen
Keyword(s):  
Open Channel ◽  
Hydraulic Performance ◽  
Aquatic Weeds ◽  
Ann Model ◽  
Channel System ◽  
The Impact

The Influence of Ascorbic Acid on Platelet Structure and Function

1975 ◽  
Vol 34 (01) ◽  
pp. 050-062
Author(s):  
Dale H Cowan ◽  
Richard C Graham ◽  
Patricia Shook ◽  
Ronda Griffin
Keyword(s):  
Ascorbic Acid ◽  
Open Channel ◽  
Channel System ◽  
Short Intervals ◽  
Platelet Survival ◽  
Artifactual Changes ◽  
Survival Studies ◽  
And Function ◽  
Induced Aggregation ◽  
Platelet Structure

SummaryTo determine the effect on platelet behavior of transient exposure of platelets to ascorbic acid, studies of platelet function and ultrastructure were done before exposure to ascorbic acid at pH 6.5, during exposure to pH 6.5, and after restoration of pH to pre-acidifìcation levels. The effect of ascorbic acid (A. A.) was compared to that of HCl and citric acid (C. A.). ADP- and collagen-induced aggregation of normal platelets were significantly impaired by both A. A. and C. A. but were less affected by HCl. The release of 14C-serotonin was significantly reduced by each agent. The ultra-structure of normal platelets brought to pH 6.5 by A.A. was normal. After neutralization, there was marked dilatation of the open channel system and loss of the disc shape. When platelets were brought to pH 6.5 by A. A., then neutralized, the aggregates which formed after stimulation by ADP or collagen were smaller than normal, the platelets were less closely approximated, and degranulation was less complete. The data show that exposure of platelets to ascorbic acid for short intervals impairs their function when measured after restoration of pH to levels compatible with maximal responses. Platelet survival studies using autologous platelets labelled with 51Cr in the presence or absence of ascorbic acid showed that the recovery of normal platelets was unaffected by ascorbic acid, whereas recovery of platelets from patients with idiopathic thrombocytopenic purpura, idiopathic thrombocythemia, and alcohol-related thrombocytopenia was markedly reduced. The injury resulting from the use of ascorbic acid in preparing platelets for studies of platelet survival in patients with disorders affecting platelets may impair the recovery of the cells, resulting in artifactual changes in the survival studies.

scholarly journals Artificial neural network, predictor variables and sensitivity threshold for DNA methylation-based age prediction using blood samples

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhonghui Thong ◽  
Jolena Ying Ying Tan ◽  
Eileen Shuzhen Loo ◽  
Yu Wei Phua ◽  
Xavier Liang Shun Chan ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Regression Model ◽  
Sensitivity Threshold ◽  
Ann Model ◽  
Blood Samples ◽  
Ann Models ◽  
Artificial Neural ◽  
The Impact ◽  
Age Prediction

AbstractRegression models are often used to predict age of an individual based on methylation patterns. Artificial neural network (ANN) however was recently shown to be more accurate for age prediction. Additionally, the impact of ethnicity and sex on our previous regression model have not been studied. Furthermore, there is currently no age prediction study investigating the lower limit of input DNA at the bisulfite treatment stage prior to pyrosequencing. Herein, we evaluated both regression and ANN models, and the impact of ethnicity and sex on age prediction for 333 local blood samples using three loci on the pyrosequencing platform. Subsequently, we trained a one locus-based ANN model to reduce the amount of DNA used. We demonstrated that the ANN model has a higher accuracy of age prediction than the regression model. Additionally, we showed that ethnicity did not affect age prediction among local Chinese, Malays and Indians. Although the predicted age of males were marginally overestimated, sex did not impact the accuracy of age prediction. Lastly, we present a one locus, dual CpG model using 25 ng of input DNA that is sufficient for forensic age prediction. In conclusion, the two ANN models validated would be useful for age prediction to provide forensic intelligence leads.

P-203 Applying artificial intelligence for ploidy prediction: The concentration of IL-6 in spent culture medium, blastocyst morphological grade and embryo morphokinetics as variables under consideration

2021 ◽  
Vol 36 (Supplement_1) ◽  
Author(s):  
B Aparicio Ruiz ◽  
L Bori ◽  
E Paya ◽  
M A Valera ◽  
A Quiñonero ◽  
...  
Keyword(s):  
Embryo Culture ◽  
Culture Medium ◽  
Culture Media ◽  
General Description ◽  
Ann Model ◽  
Non Invasive ◽  
Morphokinetic Parameters ◽  
Testing Dataset ◽  
Standard Deviations ◽  
The Impact

Abstract Study question Would it be possible to predict embryo ploidy by taking into account conventional morphological and morphokinetic parameters together with IL-6 concentration in spent culture medium? Summary answer Our artificial neural network (ANN) trained with blastocyst morphology, embryo morphokinetics and IL-6 concentration distinguished between euploid/aneuploid embryos in 65% of the testing dataset. What is known already The analysis of spent embryo culture media represents the protein and metabolic state of the embryo and could be a non-invasive method of obtaining information about embryo quality. The impact of the presence/absence of several proteins in embryo culture samples over clinical results has been widely studied. The IL-6 is one of the most mentioned protein for its effect on embryo development, implantation and likelihood of achieving a live birth. In this initial attempt, we examined the predictive value for euploidy of a model that took into account the concentration of IL-6 in the spent culture medium. Study design, size, duration This prospective study included 319 embryos with PGT-A results. Out of the total, 127 were euploid and 192 aneuploid embryos. Concentration of IL-6 in spent embryo culture media (collected on the day of trophectoderm biopsy-fifth/sixth day of development), morphokinetic parameters (division time to 2 cells-t2; to 3 cells-t3, to 4 cells-t4; to 5 cells-t5 and time of blastocyst formation-tB) and blastocyst morphological grade (according to ASEBIR criteria) were considered to predict the embryo ploidy. Participants/materials, setting, methods Embryos were cultured in EmbryoScope. The chromosome analysis was performed using next-generation sequence technology. The concentration of IL-6 was measured in 20µL of spent embryo culture media with ELISA kits. Morphokinetic parameters were automatically annotated and the blastocyst morphology was evaluated by senior embryologists based on blastocele expansion, inner cell mass and trophectoderm quality. All the embryos were divided into 70% for training, 15% for validating and 15% for testing our ANN model with MatLab®. Main results and the role of chance The general description for the euploid embryo population was the following: 2% of the embryos were graded as A, 71% were graded as B and 28% were graded as C; the means and standard deviations were 25.32±2.97 hours (h) for t2, 35.33±5.15h for t3, 37.30±5.43h for t4, 48.24±6.62h for t5 and 103.93±12.8h for tB; and the average of IL-6 concentration was 1.51±0.70 pg/ml. The general description for the aneuploid embryo population was the following: 1% of the embryos were graded as A, 48% were graded as B and 51% were graded as C; the means and standard deviations were 26.13±3.51h for t2, 36.70±4.29h for t3, 38.20±4.24h for t4, 49.86±6.89h for t5 and 107.10±8.29h for tB; and the average of IL-6 concentration was 1.47±0.71 pg/ml. Our ANN model showed a higher general success rate as we increased the variables considered in the final prediction of euploid embryos. The accuracy, sensitivity and specificity for the testing dataset were: 0.60, 0.12 and 0.87 with morphokinetic parameters; 0.63, 0.24 and 0.93 with morphokinetics and IL-6 concentration; and 0.65, 0.16 and 0.96 with morphokinetics, IL-6 concentration and blastocyst morphological grade. Limitations, reasons for caution The low sensitivity and high specificity achieved in our models indicated that they were more capable of detecting aneuploid than euploid embryos. As this was a preliminary study, the small number of embryos included in the test (n = 48) was also a limitation. Wider implications of the findings The results showed that our model tended to classify the embryos as aneuploid. More euploid embryos would be necessary to train our model and achieve better results in the prediction of chromosomally normal embryos. Further studies with large number of embryos and additional variables could improve the non-invasive ploidy prediction. Trial registration number not applicable

Machine Learning and Artificial Intelligence Provides Wolfcamp Completion Design Insight

2021 ◽  
Author(s):  
Robert Shelley ◽  
Oladapo Oduba ◽  
Howard Melcher
Keyword(s):  
Machine Learning ◽  
Water Saturation ◽  
Saturation Pressure ◽  
Ann Model ◽  
Permian Basin ◽  
Treatment Rate ◽  
Feed Forward Neural Network ◽  
Well Production ◽  
Reservoir Type ◽  
The Impact

Abstract The subject of this paper is the application of a unique machine learning approach to the evaluation of Wolfcamp B completions. A database consisting of Reservoir, Completion, Frac and Production information from 301 Multi-Fractured Horizontal Wolfcamp B Completions was assembled. These completions were from a 10-County area located in the Texas portion of the Permian Basin. Within this database there is a wide variation in completion design from many operators; lateral lengths ranging from a low of about 4,000 ft to a high of almost 15,000 ft, proppant intensities from 500 to 4,000 lb/ft and frac stage spacing from 59 to 769 ft. Two independent self-organizing data mappings (SOM) were performed; the first on completion and frac stage parameters, the second on reservoir and geology. Characteristics for wells assigned to each SOM bin were determined. These two mappings were then combined into a reservoir type vs completion type matrix. This type of approach is intended to remove systemactic errors in measuement, bias and inconsistencies in the database so that more realistic assessments about well performance can be made. Production for completion and reservoir type combinations were determined. As a final step, a feed forward neural network (ANN) model was developed from the mapped data. This model was used to estimate Wolfcamp B production and economics for completion and frac designs. In the performance of this project, it became apparent that the incorporation of reservoir data was essential to understanding the impact of completion and frac design on multi-fractured horizontal Wolfcamp B well production and economic performance. As we would expect, wells with the most permeability, higher pore pressure, effective porosity and lower water saturation have the greatest potential for hydrocarbon production. The most effective completion types have an optimum combination of proppant intensity, fluid intensity, treatment rate, frac stage spacing and perforation clustering. This paper will be of interest to anyone optimizing hydraulically fractured Wolfcamp B completion design or evaluating Permian Basin prospects. Also, of interest is the impact of reservoir and completion characteristics such as permeability, porosity, water saturation, pressure, offset well production, proppant intensity, fluid intensity, frac stage spacing and lateral length on well production and economics. The methodology used to evaluate the impact of reservoir and completion parameters for this Wolfcamp project is unique and novel. In addition, compared to other methodologies, it is low cost and fast. And though the focus of this paper is on the Wolfcamp B Formation in the Midland Basin, this approach and workflow can be applied to any formation in any Basin, provided sufficient data is available.

Comparison of two gap-filling techniques for nitrous oxide fluxes from agricultural soil

2019 ◽  
Vol 99 (1) ◽  
pp. 12-24 ◽  
Author(s):  
Rezvan Taki ◽  
Claudia Wagner-Riddle ◽  
Gary Parkin ◽  
Rob Gordon ◽  
Andrew VanderZaag
Keyword(s):  
Missing Values ◽  
Interpolation Method ◽  
Weather Conditions ◽  
Linear Interpolation ◽  
Bias Error ◽  
Research Station ◽  
Ann Model ◽  
Gap Filling ◽  
Adverse Weather ◽  
The Impact

Micrometeorological methods are ideally suited for continuous measurements of N2O fluxes, but gaps in the time series occur due to low-turbulence conditions, power failures, and adverse weather conditions. Two gap-filling methods including linear interpolation and artificial neural networks (ANN) were utilized to reconstruct missing N2O flux data from a corn–soybean–wheat rotation and evaluate the impact on annual N2O emissions from 2001 to 2006 at the Elora Research Station, ON, Canada. The single-year ANN method is recommended because this method captured flux variability better than the linear interpolation method (average R2 of 0.41 vs. 0.34). Annual N2O emission and annual bias resulting from linear and single-year ANN were compatible with each other when there were few and short gaps (i.e., percentage of missing values <30%). However, with longer gaps (>20 d), the bias error in annual fluxes varied between 0.082 and 0.344 kg N2O-N ha−1 for linear and 0.069 and 0.109 kg N2O-N ha−1 for single-year ANN. Hence, the single-year ANN with lower annual bias and stable approach over various years is recommended, if the appropriate driving inputs (i.e., soil temperature, soil water content, precipitation, N mineral content, and snow depth) needed for the ANN model are available.

scholarly journals Experimental Research of Gaseous Emissions Impact on the Performance of New-Design Cylindrical Multi-Channel Cyclone with Adjustable Half-Rings

2022 ◽  
Vol 14 (2) ◽  
pp. 902
Author(s):  
Aleksandras Chlebnikovas ◽  
Dainius Paliulis ◽  
Kristina Kilikevičienė ◽  
Artūras Kilikevičius
Keyword(s):  
Experimental Research ◽  
Gas Flow ◽  
Collection Efficiency ◽  
Fine Particulate Matter ◽  
Air Flow ◽  
Gaseous Emissions ◽  
Channel System ◽  
Flow Parameters ◽  
The Impact ◽  
Channel Type

Cyclones are widely used for separating particles from gas in energy production objects. The efficiency of conventional centrifugal air cleaning devices ranges from 85 to 90%, but the weakness of many cyclones is the low collection efficiency of particles less than 10 μm in diameter. The novelty of this work is the research of the channel-type treatment device, with few levels adapted for precipitation of fine particulate matter, acting by a centrifugal and filtration principle. Many factors have an impact on cyclone efficiency—humidity, temperature, gas (air) composition, airflow velocity and etc. Many scientists evaluated only the effect of origin and size of PM on cyclone efficiency. The effect of gas (air) composition and temperature, and humidity on the multi-channel cyclone-separator efficiency still demands contributions. Complex theoretical and experimental research on air flow parameters and the efficiency of a cylindrical eight-channel system with adjustable half-rings for removing fine-dispersive particles (<20 μm) was carried out. The impact of air humidity and temperature on air flow, and gaseous smoke components on the removal of wood ashes was analyzed. The dusty gas flow was regulated. During the experiment, the average velocity of the cyclone was 16 m/s, and the temperature was 20–50 °C. The current paper presents experimental research results of wood ash removal in an eight-channel cyclone and theoretical methodology for the calculation of airflow parameters and cyclone effectiveness.

scholarly journals Bodily abjection and the politics of resistance in Tom Paulin’s Greek tragedies

2020 ◽  
Author(s):  
Isabelle Torrance
Keyword(s):  
Open Channel ◽  
Radical Change ◽  
Status Quo ◽  
The Political ◽  
Northern Irish ◽  
The Status ◽  
Politics Of Resistance ◽  
The Impact ◽  
Political Groups ◽  
Political Actions

Abstract Tom Paulin’s Greek tragedies present extremes of bodily abjection in order to service of a politics of resistance that is tied, in each case, to the political context of the drama’s production. The Riot Act (1984), Seize the Fire (1989), and Medea (2010), share a focus on the degradation of oppressed political groups and feature characters who destabilize the status quo. Yet the impact of disruptive political actions is not ultimately made clear. We are left wondering at the conclusion of each tragedy if the momentous acts of defiance we have witnessed have any power to create systemic change within politically rigged systems. The two 1980s plays are discussed together and form a sequence, with The Riot Act overtly addressing the Northern Irish conflict and Seize the Fire encompassing a broader sweep of oppressive regimes. The politics of discrimination in Medea are illuminated by comparison with similar themes in Paulin’s Love’s Bonfire (2010). Unlike other Northern Irish adaptations of Greek tragedy, Paulin’s dramas, arrested in their political moments, present little hope for the immediate future. Yet in asking us to consider if individual sacrifice is enough to achieve radical change they maintain an open channel for political discourse.

ANN-SCS-based hybrid model in conjunction with GCM to evaluate the impact of climate change on the flow scenario of the River Subansiri

2019 ◽  
Vol 11 (4) ◽  
pp. 1150-1164
Author(s):  
Swapnali Barman ◽  
Rajib Kumar Bhattacharjya
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Hybrid Model ◽  
Curve Number ◽  
Future Trend ◽  
Land Use Land Cover ◽  
Ann Model ◽  
Non Linear ◽  
The Future ◽  
The Impact

Abstract The River Subansiri, one of the largest tributaries of the Brahmaputra, makes a significant contribution towards the discharge at its confluence with the Brahmaputra. This study aims to investigate an appropriate model to predict the future flow scenario of the river Subansiri. Two models have been developed. The first model is an artificial neural network (ANN)-based rainfall-runoff model where rainfall has been considered as the input. The future rainfall of the basin is calculated using a multiple non-linear regression-based statistical downscaling technique. The proposed second model is a hybrid model developed using ANN and the Soil Conservation Service (SCS) curve number (CN) method. In this model, both rainfall and land use/land cover have been incorporated as the inputs. The ANN models were run using time series analysis and the method selected is the non-linear autoregressive model with exogenous inputs. Using Sen's slope values, the future trend of rainfall and runoff over the basin have been analyzed. The results showed that the hybrid model outperformed the simple ANN model. The ANN-SCS-based hybrid model has been run for different land use/land cover scenarios to study the future flow scenario of the River Subansiri.

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