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Analyzing Emergence in Biological Neural Networks Using Graph Signal Processing
Published in Larry B. Rainey, O. Thomas Holland, Emergent Behavior in System of Systems Engineering, 2022
Kevin Schultz, Marisel Villafañe-Delgado, Elizabeth P. Reilly, Anshu Saksena, Grace M. Hwang
Using the undirected, positive Laplacian as the basis for a GFT we see considerable structure in the graph power spectrum (see Figure 8.3b). Harmonics 0, 32, and 59 capture the average contributions between the major functional components at each point in time. The next two contributing harmonics (1 and 2) are shown in Figure 8.3d. These harmonics resemble a pair of sinusoidal waves on the excitatory networks that are 90° out of phase. Such harmonics are characteristic of ring-like networks, and these harmonics contribute to the localized activity bumps that encode the heading of the fruit fly. In contrast, using the signed Hermitian Laplacian (not shown) appears to group the network into three regimes (0–31, 32–49, 50–59), and only has the ring-like harmonics on neurons (0–31).
Decommissioning and jobs
Published in Martin J. Pasqualetti, Nuclear Decommissioning and Society, 2019
It could be argued, for example in the case of north Wales, that a carefully planned sequence of Magnox station closures, decommissioning work, and PWR station construction at Wylfa and Trawsfynydd could ensure more or less continuous employment at a high level during the next 20 years.87Figure 5.9 attempts to portray the work-force requirements. Three separate work-forces are shown on the graph: power station operatives, construction staff, and decommissioning staff. If the Wylfa B and Trawsfynydd B nuclear power stations go ahead there will be peak construction labour requirements of 3,500 in both cases, signalling a return to a ‘boom and crash’ employment rhythm in north Wales. This may be acceptable to some local councillors and economic development officials; however, it has not been widely noted locally that the decommissioning projects required to commence at Trawsfynydd A and Wylfa A during the next decade will in themselves be major enterprises (see Figure 5.7) requiring large labour inputs and complex planning if their socioeconomic impacts are to be minimized. With four major nuclear projects being contemplated within a 20-year timespan in north Wales, the negative impacts referred to above need to be given very careful consideration. The economic consequences might not be quite as attractive as at first imagined. There would be a need for large-scale transfers of ‘travelling men’ who would move between one site and another as one violent boom is followed by the next violent crash. The effects of job poaching would be felt through the whole economy. There would be hardly any new permanent jobs for local people.88 And the prospects for any reduction in the impact of nuclear colonialism during the next century would be slim indeed.
Intuitionistic fuzzy evidential power aggregation operator and its application in multiple criteria decision-making
Published in International Journal of Systems Science, 2018
Aggregation operator is a useful tool to aid and provide more versatility in the data aggregation process (Beliakov, Bustince, James, Calvo, & Fernandez, 2012; Chiclana, Herrera, & Herrera-Viedma, 2013; Jiang, Wei, Tang, & Zhou, 2017; Mo & Deng, 2016). Compared to conventional MCDM methods, the decision method based on aggregation operators can give the comprehensive evaluation values of the alternatives by fusing all the attributes for each alternative, and then give the ranking of all alternatives. Obviously, the aggregation operators have many advantages to solve the MCDM problems. In 2001, Yager first introduced a power average (P-A) operator (Yager, 2001). This aggregation operator allows argument values to support each other in the aggregation process. One of the advantages of P-A operator is that it allows the values support each other. Due to its good ability, it has been used in a wide range of applications (Yager, 2010). This operator provides a unified framework for decision-making under uncertain environment. At present, there are many different operators to deal with uncertain values based on the P-A operator such as the visibility graph power averaging operator (Jiang, Wei, Zhan, Xie, & Zhou, 2016), the generalised power average operator (Zhou & Chen, 2012), the power geometric operator and the power ordered weighted geometric operator (Xu & Yager, 2010). However, these power aggregation operators cannot be used when the input arguments are intuitionistic fuzzy numbers (IFNs). In order to solve this problem, many researchers extended these operators to intuitionistic fuzzy environments such as the IFPA operators (Xu, 2011), generalised Atanassov's intuitionistic fuzzy power geometric average operators (Zhang, 2013), the generalised intuitionistic fuzzy power averaging operator and the generalised intuitionistic fuzzy power ordered weighted averaging operator (Zhou, Chen, & Liu, 2012). We further extend the power aggregation (IFPA) operators when dealing with the MCDM problems. The most important issue in P-A operator is to find a satisfying support function. However, Yager did not give a specific definition of the support degree. How to define the support degree is still an open issue. The main contribution of this paper is we propose a satisfying support function so that the P-A operator can effectively aggregate IFNs. At the same time, we not only apply P-A operator to single point values, but also interval values.