Research and development challenges for agent-based systems

1997 ◽  
Vol 144 (1) ◽  
pp. 2 ◽  
Author(s):  
D.T. Ndumu ◽  
H.S. Nwana
2018 ◽  
Vol 46 (S1) ◽  
pp. 32-42 ◽  
Author(s):  
Christopher Okhravi ◽  
Simone Callegari ◽  
Steve McKeever ◽  
Carl Kronlid ◽  
Enrico Baraldi ◽  
...  

We design an agent based Monte Carlo model of antibiotics research and development (R&D) to explore the effects of the policy intervention known as Market Entry Reward (MER) on the likelihood that an antibiotic entering pre-clinical development reaches the market. By means of sensitivity analysis we explore the interaction between the MER and four key parameters: projected net revenues, R&D costs, venture capitalists discount rates, and large pharmaceutical organizations' financial thresholds. We show that improving revenues may be more efficient than reducing costs, and thus confirm that this pull-based policy intervention effectively stimulates antibiotics R&D.


2022 ◽  
Vol 2159 (1) ◽  
pp. 012013
Author(s):  
J M Redondo ◽  
J S Garcia ◽  
C Bustamante-Zamudio ◽  
M F Pereira ◽  
H F Trujillo

Abstract Socio-ecological systems like another physical systems are complex systems in which are required methods for analyzes their non-linearities, thresholds, feedbacks, time lags, and resilience. This involves understanding the heterogeneity of the interactions in time and space. In this article, we carry out the proposition and demonstration of two methods that allow the calculation of heterogeneity in different contexts. The practical effectiveness of the methods is presented through applications in sustainability analysis, land transport, and governance. It is concluded that the proposed methods can be used in various research and development areas due to their ease of being considered in broad modeling frameworks as agent-based modeling, system dynamics, or machine learning, although it could also be used to obtain point measurements only by replacing values.


Author(s):  
Jorge Perdigao

In 1955, Buonocore introduced the etching of enamel with phosphoric acid. Bonding to enamel was created by mechanical interlocking of resin tags with enamel prisms. Enamel is an inert tissue whose main component is hydroxyapatite (98% by weight). Conversely, dentin is a wet living tissue crossed by tubules containing cellular extensions of the dental pulp. Dentin consists of 18% of organic material, primarily collagen. Several generations of dentin bonding systems (DBS) have been studied in the last 20 years. The dentin bond strengths associated with these DBS have been constantly lower than the enamel bond strengths. Recently, a new generation of DBS has been described. They are applied in three steps: an acid agent on enamel and dentin (total etch technique), two mixed primers and a bonding agent based on a methacrylate resin. They are supposed to bond composite resin to wet dentin through dentin organic component, forming a peculiar blended structure that is part tooth and part resin: the hybrid layer.


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