Process Uniformity During Electro-Thermal Applications and Modeling Approaches

2021 ◽  
pp. 713-730
Author(s):  
Ozan Altin ◽  
Samet Ozturk ◽  
Huseyin Topcam ◽  
Ozan Karatas ◽  
Francesco Marra ◽  
...  
Keyword(s):  
Modeling Approaches ◽  
Process Uniformity

Characterization of Structural Properties in High Reynolds Hydraulic Jump Based on CFD and Physical Modeling Approaches

2020 ◽  
Vol 146 (12) ◽  
pp. 04020079 ◽  
Author(s):  
Juan Francisco Macián-Pérez ◽  
Arnau Bayón ◽  
Rafael García-Bartual ◽  
P. Amparo López-Jiménez ◽  
Francisco José Vallés-Morán
Keyword(s):  
Structural Properties ◽  
Physical Modeling ◽  
Hydraulic Jump ◽  
Modeling Approaches ◽  
High Reynolds

Analyzing cross‐lag effects: A comparison of different cross‐lag modeling approaches

2021 ◽  
Vol 2021 (175) ◽  
pp. 11-33 ◽  
Author(s):  
Kevin J. Grimm ◽  
Jonathan Helm ◽  
Danielle Rodgers ◽  
Holly O'Rourke
Keyword(s):  
Lag Effects ◽  
Modeling Approaches ◽  
Lag Modeling

A review on modeling approaches for the electrostatic charging of particles

2021 ◽  
Author(s):  
F. Chowdhury ◽  
M. Ray ◽  
A. Sowinski ◽  
P. Mehrani ◽  
A. Passalacqua
Keyword(s):  
Modeling Approaches ◽  
Electrostatic Charging

scholarly journals Automating Uniformity Trials to Optimize Precision of Agronomic Field Trials

Agronomy ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1254
Author(s):  
Marcus Jones ◽  
Marin Harbur ◽  
Ken J. Moore
Keyword(s):  
Web Application ◽  
Unit Area ◽  
Field Trials ◽  
State University ◽  
Iowa State University ◽  
Plot Size ◽  
Maize Field ◽  
Process Uniformity ◽  
Field Plots ◽  
Uniformity Trials

Plot size has an important impact on variation among plots in agronomic field trials, but is rarely considered during the design process. Uniformity trials can inform a researcher about underlying variance, but are seldom used due to their laborious nature. The objective of this research was to describe variation in maize field trials among field plots of varying size and develop a tool to optimize field-trial design using uniformity-trial statistics. Six uniformity trials were conducted in 2015–2016 in conjunction with Iowa State University and WinField United. All six uniformity trials exhibited a negative asymptotic relationship between variance and plot size. Variance per unit area was reduced over 50% with plots 41.8 m2 in size and over 75% when using a plot size >111.5 m2 compared to a 13.9 m2 plot. Plot shape within a fixed plot size did not influence variance. The data illustrated fewer replicates were needed as plot size increased, since larger plots reduced variability. Use of a Shiny web application is demonstrated that allows a researcher to upload a yield map and consider uniformity-trial statistics to inform plot size and replicate decisions.

scholarly journals Influence of Occupant Behavior for Building Energy Conservation: A Systematic Review Study of Diverse Modeling and Simulation Approach

Buildings ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 41
Author(s):  
Mohammad Nyme Uddin ◽  
Hsi-Hsien Wei ◽  
Hung Lin Chi ◽  
Meng Ni
Keyword(s):  
Systematic Review ◽  
Energy Conservation ◽  
Building Energy ◽  
Outdoor Environment ◽  
Behavior Modeling ◽  
Physical Factors ◽  
Occupant Behavior ◽  
Modeling Approaches ◽  
Building Energy Conservation ◽  
Review Study

Energy consumption in buildings depends on several physical factors, including its physical characteristics, various building services systems/appliances used, and the outdoor environment. However, the occupants’ behavior that determines and regulates the building energy conservation also plays a critical role in the buildings’ energy performance. Compared to physical factors, there are relatively fewer studies on occupants’ behavior. This paper reports a systematic review analysis on occupant behavior and different modeling approaches using the Scopus and Science Direct databases. The comprehensive review study focuses on the current understanding of occupant behavior, existing behavior modeling approaches and their limitations, and key influential parameters on building energy conservation. Finally, the study identifies six significant research gaps for future development: occupant-centered space layout deployment; occupant behavior must be understood in the context of developing or low-income economies; there are higher numbers of quantitative occupant behavior studies than qualitative; the extensive use of survey or secondary data and the lack of real data used in model validation; behavior studies are required for diverse categories building; building information modeling (BIM) integration with existing occupant behavior modeling/simulation. These checklists of the gaps are beneficial for researchers to accomplish the future research in the built environment.

scholarly journals Leveraging artificial intelligence for pandemic preparedness and response: a scoping review to identify key use cases

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Ania Syrowatka ◽  
Masha Kuznetsova ◽  
Ava Alsubai ◽  
Adam L. Beckman ◽  
Paul A. Bain ◽  
...  
Keyword(s):  
Public Health ◽  
Artificial Intelligence ◽  
Real Time ◽  
Scoping Review ◽  
Grey Literature ◽  
Use Cases ◽  
Outbreak Detection ◽  
Response To Treatment ◽  
Pandemic Preparedness ◽  
Modeling Approaches

AbstractArtificial intelligence (AI) represents a valuable tool that could be widely used to inform clinical and public health decision-making to effectively manage the impacts of a pandemic. The objective of this scoping review was to identify the key use cases for involving AI for pandemic preparedness and response from the peer-reviewed, preprint, and grey literature. The data synthesis had two parts: an in-depth review of studies that leveraged machine learning (ML) techniques and a limited review of studies that applied traditional modeling approaches. ML applications from the in-depth review were categorized into use cases related to public health and clinical practice, and narratively synthesized. One hundred eighty-three articles met the inclusion criteria for the in-depth review. Six key use cases were identified: forecasting infectious disease dynamics and effects of interventions; surveillance and outbreak detection; real-time monitoring of adherence to public health recommendations; real-time detection of influenza-like illness; triage and timely diagnosis of infections; and prognosis of illness and response to treatment. Data sources and types of ML that were useful varied by use case. The search identified 1167 articles that reported on traditional modeling approaches, which highlighted additional areas where ML could be leveraged for improving the accuracy of estimations or projections. Important ML-based solutions have been developed in response to pandemics, and particularly for COVID-19 but few were optimized for practical application early in the pandemic. These findings can support policymakers, clinicians, and other stakeholders in prioritizing research and development to support operationalization of AI for future pandemics.

Sign in / Sign up

Export Citation Format

Share Document