Application of a 3-D chemical fate prediction model (FATE3D) to predict dioxin concentrations in the Tokyo Bay

2006 ◽  
Vol 70 (4) ◽  
pp. 621-632 ◽  
Author(s):  
Norihiro Kobayashi ◽  
Tomomi Eriguchi ◽  
Kisaburo Nakata ◽  
Shigeki Masunaga ◽  
Fumio Horiguchi ◽  
...  
Keyword(s):  
Prediction Model ◽  
Tokyo Bay ◽  
Chemical Fate

1575: External Validation of Outcome Prediction Model for Ureteral/Renal Calculi

2005 ◽  
Vol 173 (4S) ◽  
pp. 427-427
Author(s):  
Sijo J. Parekattil ◽  
Udaya Kumar ◽  
Nicholas J. Hegarty ◽  
Clay Williams ◽  
Tara Allen ◽  
...  
Keyword(s):  
Prediction Model ◽  
Outcome Prediction ◽  
External Validation ◽  
Renal Calculi

Development of a headlamp dimming request prediction model

1975 ◽  
Author(s):  
Vivek D. Bhise ◽  
Thomas F. Swigart ◽  
Eugene I. Farber
Keyword(s):  
Prediction Model

Short-Version YLS-CMI: An Alternative Prediction Model for Juvenile Offenders at Intake

2009 ◽  
Author(s):  
Christina Campbell ◽  
Eyitayo Onifade ◽  
William Davidson ◽  
Jodie Petersen
Keyword(s):  
Juvenile Offenders ◽  
Prediction Model ◽  
Short Version

Career Choice Prediction--Model

2019 ◽  
Author(s):  
Zool Hilmi Mohamed Ashari ◽  
Norzaini Azman ◽  
Mohamad Sattar Rasul
Keyword(s):  
Prediction Model ◽  
Career Choice

Production of asari (Manila) clams, Ruditapes philippinarum, during the period of harvest decrease in the 2000s in the Banzu tidal area, Tokyo Bay

2020 ◽  
Vol 33 ◽  
pp. 14
Author(s):  
Mitsuharu Toba ◽  
Jun Kakino ◽  
Kazuo Tada ◽  
Yutaka Kobayashi ◽  
Hideharu Tsuchie
Keyword(s):  
Field Investigation ◽  
Ruditapes Philippinarum ◽  
Management Style ◽  
Seed Stock ◽  
Tokyo Bay ◽  
Yearly Variation ◽  
Culture Area ◽  
Stock Biomass ◽  
Tidal Area ◽  
High Level

In Tokyo Bay, the harvestable quantity of asari (Manila) clams Ruditapes philippinarum has been decreasing since the late 1990s. We conducted a field investigation on clam density in the Banzu culture area from April 1988 to December 2014 and collected records spanning January 1986 to September 2017 from relevant fisheries cooperative associations to clarify the relationship between the temporal variation in stock abundance and the production activities of fishermen. The yearly variation in clam abundance over the study period was marked by larger decreases in the numbers of larger clams. A large quantity of juvenile clams, beyond the biological productivity of the culture area, may have been introduced as seed stock in the late 1980s despite the high level of harvestable stock. The declines in harvested quantity began in the late 1990s and may have been caused by decreases in harvestable stock despite the continuous addition of seed stock clams. The harvested quantity is likely to be significantly dependent upon the wild clam population, even within the culture area, as the harvestable quantity was not correlated with the quantity of seed stock introduced during the study period. These declines in harvested quantity may have resulted from a decreasing number of operating harvesters due to the low level of harvestable stock and consequently reduced profitability. Two findings were emphasized. A certain management style, based on predictions of the contributions of wild and introduced clams to future stock biomass, is essential for economically-feasible culturing. In areas with less harvestable stock, actions should be taken to maintain the incomes of harvesters while avoiding overexploitation, even if the total harvest quantity decreases.

Speed Prediction Model of Trucks on Gradient Sections Considering Vertical Curve

CICTP 2020 ◽  
2020 ◽  
Author(s):  
Qianqian Liang ◽  
Xiaodong Zhang ◽  
Jinliang Xu ◽  
Yang Zhang
Keyword(s):  
Prediction Model ◽  
Speed Prediction

Stock Market Prediction using Data Mining Techniques

2020 ◽  
pp. 310-313
Author(s):  
Karunesh Makker ◽  
Prince Patel ◽  
Hrishikesh Roy ◽  
Sonali Borse
Keyword(s):  
Neural Network ◽  
Stock Market ◽  
Prediction Model ◽  
Efficient Market Hypothesis ◽  
Deterministic System ◽  
Vast Number ◽  
Factors Influencing ◽  
Number Of Factors ◽  
Using Data ◽  
Robust Prediction

Stock market is a very volatile in-deterministic system with vast number of factors influencing the direction of trend on varying scales and multiple layers. Efficient Market Hypothesis (EMH) states that the market is unbeatable. This makes predicting the uptrend or downtrend a very challenging task. This research aims to combine multiple existing techniques into a much more robust prediction model which can handle various scenarios in which investment can be beneficial. Existing techniques like sentiment analysis or neural network techniques can be too narrow in their approach and can lead to erroneous outcomes for varying scenarios. By combing both techniques, this prediction model can provide more accurate and flexible recommendations. Embedding Technical indicators will guide the investor to minimize the risk and reap better returns.

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