Artificial-intelligence-based prediction and control of combustion instabilities in spark-ignition engines

2022 ◽  
pp. 185-212
Author(s):  
Bryan Maldonado ◽  
Anna Stefanopoulou ◽  
Brian Kaul
Keyword(s):  
Artificial Intelligence ◽  
Spark Ignition ◽  
Spark Ignition Engines ◽  
Combustion Instabilities ◽  
Prediction And Control ◽  
And Control

scholarly journals Spread of Covid-19 in the United States is controlled

2020 ◽  
Author(s):  
Zixin Hu ◽  
Qiyang Ge ◽  
Shudi Li ◽  
Tao Xu ◽  
Eric Boerwinkle ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
The United States ◽  
Peak Time ◽  
Time Prediction ◽  
Intervention Measures ◽  
The Us ◽  
Prediction And Control ◽  
Integrated Methods ◽  
And Control ◽  
The Impact

AbstractAs of May 1, 2020, the number of cases of Covid-19 in the US passed 1,062,446, interventions to slow down the spread of Covid-19 curtailed most social activities. Meanwhile, an economic crisis and resistance to the strict intervention measures are rising. Some researchers proposed intermittent social distancing that may drive the outbreak of Covid-19 into 2022. Questions arise about whether we should maintain or relax quarantine measures. We developed novel artificial intelligence and causal inference integrated methods for real-time prediction and control of nonlinear epidemic systems. We estimated that the peak time of the Covid-19 in the US would be April 24, 2020 and its outbreak in the US will be over by the end of July and reach 1,551,901 cases. We evaluated the impact of relaxing the current interventions for reopening economy on the spread of Covid-19. We provide tools for balancing the risks of workers and reopening economy.

An insight into Case Management and Challenges in Veteran Suicide Prevention

2020 ◽  
pp. 14-25
Author(s):  
Tamara Green
Keyword(s):  
Case Management ◽  
Suicide Prevention ◽  
Suicide Risk ◽  
Digital Transformation ◽  
Prevention Science ◽  
Historical Records ◽  
Military Members ◽  
Prediction And Control ◽  
And Control ◽  
Insight Into

Much of the literature, policies, programs, and investment has been made on mental health, case management, and suicide prevention of veterans. The Australian “veteran community is facing a suicide epidemic for the reasons that are extremely complex and beyond the scope of those currently dealing with them.” (Menz, D: 2019). Only limited work has considered the digital transformation of loosely and manual-based historical records and no enablement of Artificial Intelligence (A.I) and machine learning to suicide risk prediction and control for serving military members and veterans to date. This paper presents issues and challenges in suicide prevention and management of veterans, from the standing of policymakers to stakeholders, campaigners of veteran suicide prevention, science and big data, and an opportunity for the digital transformation of case management.

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