Immediate and Continued Effects of App Real-Time Targeting along Customer Shopping Journey: A Field Experiment

2021 ◽  
Author(s):  
Siliang Tong ◽  
Le Wang ◽  
Xueming Luo ◽  
Takeshi Moriguchi
Keyword(s):  
Field Experiment ◽  
Real Time

scholarly journals Yield and Yield Attributing Parameters of Toria (Brassica campestries) under Real Time Rainfall Situation in an Inceptisols of Assam, India

2020 ◽  
pp. 311-321
Author(s):  
R. Borah ◽  
N. Baruah ◽  
P. K. Sarma ◽  
R. Borah ◽  
A. Sonowal ◽  
...  
Keyword(s):  
Field Experiment ◽  
Real Time ◽  
Seed Yield ◽  
Seed Weight ◽  
Growth Yield ◽  
Rabi Season ◽  
Higher Plant ◽  
College Of Agriculture ◽  
Sowing Dates ◽  
Stover Yield

A field experiment was conducted during rabi season of 2018-19 and 2019-20 in Dryland experimental field belong to soil order Inceptisols, Biswanath college of Agriculture, Assam Agricultural University, Biswanath chariali, Assam to study the ‘‘Yield and yield attributing parameters of toria (Brassica campestries) under real time rainfall situation in an Inceptisols of Assam, India’’ under AICRPDA, NICRA. The treatments consisting of 4 different dates of sowing i.e. S1-41th SMW, S2-44th SMW, S3-46th SMW, and S4- 48th SMW, & three variety i.e. V1-JT-90-1(Jeuti), V2-Yellow sarson (Benoy) and V3- TS-38. Growth, yield and yield attributing characters of toria varieties were influenced by different dates of sowing. S1 registered higher plant height (43.2 cm, 92.9 cm and 106.6 cm & 40.2 cm, 89.8 cm and 101.5 cm) and number of branch (3.8, 5.3 and 7.2 & 3.4, 5.1 and 6.9) at 30 DAS, 45 DAS and 60 DAS, respectively, during 2018-19 and 2019-20. Yield attributing characters like number of siliqua, number of seed per siliqua, 1000 seed weight (g) were gradually decreased with advancement of sowing dates. Among the three varieties V1 (Jeuti) recorded highest seed yield (8.9 q ha-1 and 8.1 q ha-1) and stover yield (23.4 q ha-1 and 22.2 q ha-1) in 2018-19 and 2019-20, respectively. Highest HI (28.5% and 25.8%) was recorded in S1 and lowest was recorded in S4 (20.7% and 14.6%).

Effect of LCC-based nitrogen application on growth and yieldof rice (Oryza sativa L.) varieties during dry season

2016 ◽  
Vol 51 (1) ◽  
Author(s):  
Shrabani Moharana ◽  
J.M. L. Gulati ◽  
S. N. Jena
Keyword(s):  
Oryza Sativa ◽  
Field Experiment ◽  
Grain Yield ◽  
Real Time ◽  
Oryza Sativa L ◽  
Threshold Value ◽  
Nitrogen Management ◽  
Nitrogen Application ◽  
Panicle Length ◽  
Straw Yield

Data from a field experiment on Real Time Nitrogen Management (RTNM) in rice revealed that variety Gobinda produced significantly the highest grain yield of 49.6 q ha-1 associated with long panicle (26.75 cm) bearing significantly the maximum number of filled grains panicle-1 (156.78) producing highest net return (Rs.33214.71), B-C ratio (1.83) and return per rupee invested (0.83). Application of nitrogen based on LCC threshold value 4 produced significantly the highest grain (52.6 q ha-1), straw yield (64.4 q ha-1), number of EBT m-2 (403.71), panicle length (25.43 cm) and 148.94 filled grain panicle-1. Variety x RTNM interaction was significant and variety Naveen and Gobinda produced significantly the highest yield of 55.4 and 58.2 q ha-1 at recommended of nitrogen whereas, Lalat and Hiranmayee responded to LCC threshold value 4 (N4) with grain yield of 50.4 and 52.1 q ha-1, respectively.

A real-time field experiment on search and rescue operations assisted by unmanned aerial vehicles

2018 ◽  
Vol 35 (6) ◽  
pp. 906-920 ◽  
Author(s):  
Tomasz Niedzielski ◽  
Mirosława Jurecka ◽  
Bartłomiej Miziński ◽  
Joanna Remisz ◽  
Jacek Ślopek ◽  
...  
Keyword(s):  
Field Experiment ◽  
Unmanned Aerial Vehicles ◽  
Real Time ◽  
Search And Rescue ◽  
Aerial Vehicles

Analysis of Strain Sensor Cable Models and Effective Deployments for Distributed Fiber Optical Geotechnical Monitoring System

2015 ◽  
Author(s):  
Dana DuToit ◽  
Kent Ryan ◽  
John Rice ◽  
James Bay ◽  
Fabien Ravet
Keyword(s):  
Field Experiment ◽  
Real Time ◽  
Fiber Optic ◽  
Fiber Optic Sensors ◽  
Operating Conditions ◽  
Fiber Optic Sensor ◽  
Sensor Data ◽  
Deep Trench ◽  
Geotechnical Monitoring ◽  
Alternative Scenarios

Long range, distributed fiber optic sensing systems have been an available tool for more than a decade to monitor pipeline subsidence integrity challenges. Effective deployment scenarios are an important decision to be factored into the selection of this monitoring equipment and typologies relative to specific project needs. In an effort to analyze the effectiveness of various fiber optic deployment conditions, a controlled field experiment was conducted. Within this field experiment, a variety of distributed fiber optic sensors and point sensors were deployed in predefined positions. These positions relative to the pipeline were selected to support a range of deployment needs including new construction or retrofitting of existing pipelines. A 16-inch diameter by 60-meter long epoxy coated pipeline that was capable of being pressurized to mimic operating conditions was utilized. This test pipe was installed in a typical trench setting. Conventional point gauges were installed at key locations on the pipeline. Fiber optic sensor cables were installed at key locations providing 14 alternative scenarios in terms of sensitivity, accuracy, and cost. After construction of the test pipeline, real time continuous monitoring via the array of conventional and fiber optic sensors commenced. A deep trench was excavated adjacent and parallel to the central portion of the pipeline which began to induce subsidence in the test pipeline. Continued monitoring of the various sensors produced real time visualization of the evolving subsidence. A comparison of the reaction of the sensors is compiled to provide an intelligent selection criteria for integrity managers in terms of accuracy, deployment, and costs for pipeline subsidence monitoring projects. In addition, further analysis of this sensor data should provide more insight into pipeline/soil interaction models and behaviors.

Fiber Optic Monitoring of Pipeline Subsidence: Analysis of Effective Sensor Cable Deployments

2014 ◽  
Author(s):  
Dana DuToit ◽  
Kent Ryan ◽  
John Rice ◽  
James Bay ◽  
Jorge Peralta
Keyword(s):  
Field Experiment ◽  
Real Time ◽  
Fiber Optic ◽  
Fiber Optic Sensors ◽  
Operating Conditions ◽  
Fiber Optic Sensor ◽  
Sensor Data ◽  
Deep Trench ◽  
Real Time Visualization ◽  
Alternative Scenarios

Long range, distributed fiber optic sensing systems have been an available tool for more than a decade to monitor pipeline subsidence integrity challenges. Effective deployment scenarios are an important decision to be factored into the selection of this monitoring equipment and typologies relative to specific project needs. In an effort to analyze the effectiveness of various fiber optic deployment conditions, a controlled field experiment was conducted. Within this field experiment, a variety of distributed fiber optic sensors and point sensors were deployed in predefined positions. These positions relative to the pipeline were selected to support a range of deployment needs including new construction or retrofitting of existing pipelines. A 16-inch diameter by 60-meter long epoxy coated pipeline that was capable of being pressurized to mimic operating conditions was utilized. This test pipe was installed in a typical trench setting. Conventional point gauges were installed at key locations on the pipeline. Fiber optic sensor cables were installed at key locations providing 14 alternative scenarios in terms of sensitivity, accuracy, and cost. After construction of the test pipeline, real time continuous monitoring via the array of conventional and fiber optic sensors commenced. A deep trench was excavated adjacent and parallel to the central portion of the pipeline which began to induce subsidence in the test pipeline. Continued monitoring of the various sensors produced real time visualization of the evolving subsidence. A comparison of the reaction of the sensors is compiled to provide an intelligent selection criteria for integrity managers in terms of accuracy, deployment, and costs for pipeline subsidence monitoring projects. In addition, further analysis of this sensor data should provide more insight into pipeline/soil interaction models and behaviors.

scholarly journals Artificial Neural Network-Based Method for Real-Time Estimation of Compaction Quality of Hot Asphalt Mixes

2021 ◽  
Vol 11 (15) ◽  
pp. 7136
Author(s):  
Zhichao Xue ◽  
Weidong Cao ◽  
Shutang Liu ◽  
Fei Ren ◽  
Qilun Wu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Field Experiment ◽  
Real Time ◽  
Layer Structure ◽  
Ann Models ◽  
Artificial Neural ◽  
Hidden Layer ◽  
Ann Classifier

With the advancement of intelligent compaction technology, real-time quality control has been widely investigated on the subgrade, while it is insufficient on asphalt pavement. This paper aims to estimate the real-time compaction quality of hot mix asphalt (HMA) using an artificial neural network (ANN) classifier. A field experiment of HMA compaction was designed. The vibration patterns of the drum were identified by using the ANN classifier and classified based on the compaction levels. The vibration signals were collected and the degree of compaction was measured in the field experiment. The collected signals were processed and the features of vibration patterns were extracted. The processed signals were tagged with their corresponding compaction level to form the sample dataset to train the ANN models. Four ANN models with different hidden layer setups were considered to investigate the effect of hidden layer structure on performance. To test the performance of the ANN classifier, the predictions made by ANN were compared with the measuring results from a non-nuclear density gauge (NNDG). The testing results show that the ANN classifier has good performance and huge potential for estimating the compaction quality of HMA in real-time.

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