scholarly journals Complex interaction processes we need to visualize that successfully fill the quantum cup of a detector

2019 ◽  
Author(s):  
Chandrasekhar Roychoudhuri ◽  
Narasimha S. Prasad
Keyword(s):  
Complex Interaction ◽  
Interaction Processes

scholarly journals The Construction of (Non-)Responsibility in Governance Networks

SAGE Open ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 215824402110306
Author(s):  
Rolien Willmes ◽  
Margit van Wessel
Keyword(s):  
Network Governance ◽  
Complex Interaction ◽  
Proof Of Concept ◽  
Formal Institutions ◽  
Interaction Processes ◽  
Governance Networks

The notion of governance networks urges study of complex interaction processes rather than formal institutions. We argue that the study of interaction in governance networks should be widened to include different ways in which interaction shapes network outcomes. Here, we focus on a dimension of governance networks that is understood as fundamental and problematic, but that as yet has not been researched through the study of interaction: the construction of responsibility in such networks. We propose a model for the analysis of responsibility construction through (implicit) negotiation on the three proposed elements of responsibility and their links: stakeholder identities, contributions to reform, and norms. This model called “the triangle of responsibility in network governance” is the main result of our research. In addition, we offer an empirical case that illustrates both the problem of responsibility construction in network governance and the usefulness of our model, thereby providing proof of concept.

Microvascular (dys)Function in Stable Coronary Artery Disease: Cross Talk with Epicardial Segments

2018 ◽  
Vol 24 (25) ◽  
pp. 2900-2905
Author(s):  
Lucian Calmac ◽  
Vlad Bataila ◽  
Bogdan Dragoescu ◽  
Cosmin Mihai ◽  
Alexandru Scafa-Udriste ◽  
...  
Keyword(s):  
Coronary Flow ◽  
Stable Coronary Artery Disease ◽  
Microvascular Dysfunction ◽  
Complex Interaction ◽  
Diagnostic Tools ◽  
Pathological State ◽  
Hemodynamic Parameters ◽  
Artery Disease ◽  
Epicardial Stenosis ◽  
Current Guidelines

Myocardial ischemia is the consequence of an unbalance between coronary flow that can be achieved and myocardial metabolic needs. Pathological state of both epicardial and intramyocardial vessels may be responsible for inducing ischemia. However, revascularization decision should be based on the severity of each epicardial lesion that is evaluated. There are different diagnostic tools that may help for the evaluation of each compartment which is based on the measurement of coronary hemodynamics. Pressure-derived indices are recommended by current guidelines for evaluation of epicardial stenosis significance. We assess the complex interaction between hemodynamic parameters in order to understand how different parameters are influenced in the settings of microvascular dysfunction.

scholarly journals Predicting Excavation-Induced Tunnel Response by Process-Based Modelling

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Linlong Mu ◽  
Jianhong Lin ◽  
Zhenhao Shi ◽  
Xingyu Kang
Keyword(s):  
Soil Properties ◽  
Urban Areas ◽  
Field Measurements ◽  
Inverse Modelling ◽  
Machine Learning Techniques ◽  
Interaction Patterns ◽  
Support Structures ◽  
Interaction Processes ◽  
Excavation Support ◽  
Artificial Neural Network Ann

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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