scholarly journals Machine Learning Analysis of the Relationship Between Changes in Immunological Parameters and Changes in Resistance to Listeria monocytogenes: A New Approach for Risk Assessment and Systems Immunology

2012 ◽  
Vol 129 (1) ◽  
pp. 57-73 ◽  
Author(s):  
Zhifa Liu ◽  
Changhe Yuan ◽  
Stephen B. Pruett
2019 ◽  
Author(s):  
F. Silva ◽  
S. Fernandes ◽  
J. Casacão ◽  
C. Libório ◽  
J. Almeida ◽  
...  

2021 ◽  
Author(s):  
Ayrat Fakhrylgayanov ◽  
Azrin Aik Jun Soh ◽  
Ahmed Osman

Abstract Conventionally offset well studies are performed by individuals where the results depend very much on visual perception, interpretation, and experience. In the specific cases for predicting the dogleg severity (DLS) output, the offset well study will take time proportionate to the volume of input, with the results being averaged out and contain high tolerances. In specific projects, these tolerances are larger than accepted, encouraging the service provider to utilize conservative solutions such as rotary steerable system (RSS) with high DLS capability in order to reduce the residual risks. These solutions can often be more costly in terms of maintenance and may add unnecessary tortuosity to the hole leading to issues during execution. This paper explores the concept of using machine learning (ML) to perform offset well study and defining key parameters affecting the DLS output. This concept consolidates the vast volumes of data that have been acquired while drilling and defines the relationship of each parameter to the final output of DLS. The first analysis reviewed five offset wells and found a multivariable correlation between applied drilling parameters to the DLS output. This correlation was then applied in 6 boreholes (3 multilateral wells), observing consistent DLS output increase by 50% using the same technology and optimal drilling parameters. The second analysis uses the same process to determine a planning DLS limit in a curve section over different formations. This paper demonstrates the potential of ML in offset well studies and beyond to predict behavior and define the relationship in a big data environment.


2017 ◽  
Vol 8 (1) ◽  
pp. 22-48 ◽  
Author(s):  
Iain Mackinnon

This article employs a new approach to studying internal colonialism in northern Scotland during the 18th and 19th centuries. A common approach to examining internal colonial situations within modern state territories is to compare characteristics of the internal colonial situation with attested attributes of external colonial relations. Although this article does not reject the comparative approach, it seeks to avoid criticisms that this approach can be misleading by demonstrating that promoters and managers of projects involving land use change, territorial dispossession and industrial development in the late modern Gàidhealtachd consistently conceived of their work as projects of colonization. It further argues that the new social, cultural and political structures these projects imposed on the area's indigenous population correspond to those found in other colonial situations, and that racist and racialist attitudes towards Gaels of the time are typical of those in colonial situations during the period. The article concludes that the late modern Gàidhealtachd has been a site of internal colonization where the relationship of domination between colonizer and colonized is complex, longstanding and occurring within the imperial state. In doing so it demonstrates that the history and present of the Gaels of Scotland belongs within the ambit of an emerging indigenous research paradigm.


Author(s):  
B. A. Dattaram ◽  
N. Madhusudanan

Flight delay is a major issue faced by airline companies. Delay in the aircraft take off can lead to penalty and extra payment to airport authorities leading to revenue loss. The causes for delays can be weather, traffic queues or component issues. In this paper, we focus on the problem of delays due to component issues in the aircraft. In particular, this paper explores the analysis of aircraft delays based on health monitoring data from the aircraft. This paper analyzes and establishes the relationship between health monitoring data and the delay of the aircrafts using exploratory analytics, stochastic approaches and machine learning techniques.


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