Fermions in three-dimensional spinfoam quantum gravity

We study the coupling of massive fermions to the quantum mechanical dynamics of spacetime emerging from the spinfoam approach in three dimensions. We first recall the classical theory before constructing a spinfoam model of quantum gravity coupled to spinors. The technique used is based on a finite expansion in inverse fermion masses leading to the computation of the vacuum to vacuum transition amplitude of the theory. The path integral is derived as a sum over closed fermionic loops wrapping around the spinfoam. The effects of quantum torsion are realised as a modification of the intertwining operators assigned to the edges of the two-complex, in accordance with loop quantum gravity. The creation of non-trivial curvature is modelled by a modification of the pure gravity vertex amplitudes. The appendix contains a review of the geometrical and algebraic structures underlying the classical coupling of fermions to three dimensional gravity.Comment: 40 pages, 3 figures, version accepted for publication in GER

Integrative Complexity: An Alternative Measure for System Modularity

Complexity and modularity are important inherent properties of the system. Complexity is the property of the system that has to do with individual system elements and their connective relationship, while modularity is the degree to which a system is made up of relatively independent but interacting elements, with each module typically carrying an isolated set of functionality. Modularization is not necessarily a means of reducing intrinsic complexity of the system but is a mechanism for complexity redistribution that can be better managed by enabling design encapsulation. In this paper, the notion of integrative complexity (IC) is proposed, and the corresponding metric is proposed as an alternative metric for modularity from a complexity management viewpoint. It is also demonstrated using several engineered systems from different application d omains that there is a strong negative correlation between the IC and system modularity. This leads to the conclusion that the IC can be used as an alternative metric for modularity assessment of system architectures.Korea (South). Ministry of Education, Science and Technology (MEST) (National Research Foundation of Korea. Grant NRF2016R1D1A1A09916273

Correlating Integrative Complexity With System Modularity

Modularity is the degree to which a system is made up of relatively independent but interacting elements. Modularization is not necessarily a means of reducing intrinsic complexity of the system, but it is a means of effectively redistributing the total complexity across the system. High degree of modularization enable reductionist strategies of system development and is an effective mechanism for complexity redistribution that can be better managed by system developers by enabling design encapsulation. In this paper, we introduce a complexity attribution framework to enable consistent complexity accounting and management procedure and show that integrative complexity has a strong inverse relationship with system modularity and its implication on complexity management for engineered system design and development.Korea (South). Ministry of Education, Science and Technology (MEST) (National Research Foundation of Korea. NRF-2016R1D1A1A09916273

A network science-based assessment methodology for robust modular system architectures during early conceptual design

This article describes a methodology to assess, during the early conceptual design stage, the robustness, and modularity of engineering system architectures, which integrates concepts from network science with engineering systems. The application specifically focuses on the architecture of the power, propulsion, and cooling systems of a naval ship. The methodology incorporates a binary Design Structure Matrix as the basis for an assessment of redundancy and modularity effects on robustness, in response to disruption of modules in the architecture. Robustness is used to drive the module selection, which supports the formulation of a robust module configuration subject to the level of redundancy in the system architecture. The case study results demonstrated: redundancy promotes robustness of the architecture and enables modularity; however, high levels of redundancy in comparison to medium level redundancy does not significantly improve robustness. The novel contribution of this article relates to the combined quantitative assessment of redundancy, modularity and robustness in a collective methodology. This methodology supports conceptual design decision making, allowing early prediction of compliance of requirements that enable cost, development time and survivability targets to be achieved

Hierarchical communities in the walnut structure of the Japanese production network

This paper studies the structure of the Japanese production network, which includes one million firms and five million supplier-customer links. This study finds that this network forms a tightly-knit structure with a core giant strongly connected component (GSCC) surrounded by IN and OUT components constituting two half-shells of the GSCC, which we call a\textit{walnut} structure because of its shape. The hierarchical structure of the communities is studied by the Infomap method, and most of the irreducible communities are found to be at the second level. The composition of some of the major communities, including overexpressions regarding their industrial or regional nature, and the connections that exist between the communities are studied in detail. The findings obtained here cause us to question the validity and accuracy of using the conventional input-output analysis, which is expected to be useful when firms in the same sectors are highly connected to each other.Comment: 38 pages, 17 figure

Survey on Neuro-Fuzzy systems and their applications in technical diagnostics and measurement

Both fuzzy logic, as the basis of many inference systems, and Neural Networks, as a powerful computational model for classification and estimation, have been used in many application fields since their birth. These two techniques are somewhat supplementary to each other in a way that what one is lacking of the other can provide. This led to the creation of Neuro-Fuzzy systems which utilize fuzzy logic to construct a complex model by extending the capabilities of Artificial Neural Networks. Generally speaking all type of systems that integrate these two techniques can be called Neuro-Fuzzy systems. Key feature of these systems is that they use input-output patterns to adjust the fuzzy sets and rules inside the model. The paper reviews the principles of a Neuro-Fuzzy system and the key methods presented in this field, furthermore provides survey on their applications for technical diagnostics and measurement. © 2015 Elsevier Ltd

Applying case based reasoning for prioritizing areas of business management

Determining the importance of different management areas in a company provides guidance about the needs of increasing the analysis and actions focuses in particular topic. To do it, it is necessary to decompose the management in a coherent set of specific management areas and provide a way that allows the company to determine the importance of these areas for them. This paper presents a novel system that guides companies to obtain a classification of important management areas for them. It is focused on the use of a case based reasoning system because the variability and the evolution of companies as time passes requires using techniques with learning capabilities. The proposed system provides an automatic self-assessment system that provides companies an ordered list of their most important management areas. This system was implemented a year ago for the evaluation of Spanish companies. Currently, it is in production providing relevant information about the management areas of these companies