Markovian Interaction Processes with Finite Range Interactions

Potential damages to existing tunnels represent a major concern for constructing deep excavations in urban areas. The uncertainty of subsurface conditions and the nonlinear interactions between multiple agents (e.g., soils, excavation support structures, and tunnel structures) make the prediction of the response of tunnel induced by adjacent excavations a rather difficult and complex task. This paper proposes an initiative to solve this problem by using process-based modelling, where information generated from the interaction processes between soils, structures, and excavation activities is utilized to gradually reduce uncertainty related to soil properties and to learn the interaction patterns through machine learning techniques. To illustrate such a concept, this paper presents a simple process-based model consisting of artificial neural network (ANN) module, inverse modelling module, and mechanistic module. The ANN module is trained to learn and recognize the patterns of the complex interactions between excavation deformations, its geometries and support structures, and soil properties. The inverse modelling module enables a gradual reduction of uncertainty associated with soil characterizations by accumulating field observations during the construction processes. Based on the inputs provided by the former two modules, the mechanistic module computes the response of tunnel. The effectiveness of the proposed process-based model is evaluated against high-fidelity numerical simulations and field measurements. These evaluations suggest that the strategy of combining artificial intelligence techniques with information generated during interaction processes can represent a promising approach to solve complex engineering problems in conventional industries.

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Finite-size instabilities in finite-range forces

Physical Review C ◽

10.1103/physrevc.103.064314 ◽

2021 ◽

Vol 103 (6) ◽

Author(s):

C. Gonzalez-Boquera ◽

M. Centelles ◽

X. Viñas ◽

L. M. Robledo

Keyword(s):

Finite Size ◽

Finite Range

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Hierarchical network models for exchangeable structured interaction processes

Journal of the American Statistical Association ◽

10.1080/01621459.2021.1896526 ◽

2021 ◽

pp. 1-43

Author(s):

Walter Dempsey ◽

Brandon Oselio ◽

Alfred Hero

Keyword(s):

Network Models ◽

Hierarchical Network ◽

Interaction Processes

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The Role of Macrophages in the Host’s Defense against Sporothrix schenckii

Pathogens ◽

10.3390/pathogens10070905 ◽

2021 ◽

Vol 10 (7) ◽

pp. 905

Author(s):

Estela Ruiz-Baca ◽

Armando Pérez-Torres ◽

Yolanda Romo-Lozano ◽

Daniel Cervantes-García ◽

Carlos A. Alba-Fierro ◽

...

Keyword(s):

Immune Responses ◽

Macrophage Polarization ◽

Sporothrix Schenckii ◽

Macrophage Cell ◽

Cell Recruitment ◽

Host Pathogen Interaction ◽

Interaction Processes ◽

Host Pathogen ◽

Molecular Patterns

The role of immune cells associated with sporotrichosis caused by Sporothrix schenckii is not yet fully clarified. Macrophages through pattern recognition receptors (PRRs) can recognize pathogen-associated molecular patterns (PAMPs) of Sporothrix, engulf it, activate respiratory burst, and secrete pro-inflammatory or anti-inflammatory biological mediators to control infection. It is important to consider that the characteristics associated with S. schenckii and/or the host may influence macrophage polarization (M1/M2), cell recruitment, and the type of immune response (1, 2, and 17). Currently, with the use of new monocyte-macrophage cell lines, it is possible to evaluate different host–pathogen interaction processes, which allows for the proposal of new mechanisms in human sporotrichosis. Therefore, in order to contribute to the understanding of these host–pathogen interactions, the aim of this review is to summarize and discuss the immune responses induced by macrophage-S. schenckii interactions, as well as the PRRs and PAMPs involved during the recognition of S. schenckii that favor the immune evasion by the fungus.

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Markov properties of cluster processes

Advances in Applied Probability ◽

10.2307/1428060 ◽

1996 ◽

Vol 28 (2) ◽

pp. 346-355 ◽

Cited By ~ 15

Author(s):

A. J. Baddeley ◽

M. N. M. Van Lieshout ◽

J. Møller

Keyword(s):

Poisson Process ◽

Point Process ◽

Markov Property ◽

Nearest Neighbour ◽

Finite Range ◽

Cluster Process ◽

Cluster Point Process ◽

Poisson Cluster ◽

Markov Properties ◽

Uniformly Bounded

We show that a Poisson cluster point process is a nearest-neighbour Markov point process [2] if the clusters have uniformly bounded diameter. It is typically not a finite-range Markov point process in the sense of Ripley and Kelly [12]. Furthermore, when the parent Poisson process is replaced by a Markov or nearest-neighbour Markov point process, the resulting cluster process is also nearest-neighbour Markov, provided all clusters are non-empty. In particular, the nearest-neighbour Markov property is preserved when points of the process are independently randomly translated, but not when they are randomly thinned.

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