Graphing Culture Change in North American Archaeology

Documentation, analysis, and explanation of culture change have long been goals of archaeology. The earliest archaeological spindle graphs appeared in the 1880s and 1890s, but had no influence on subsequent archaeologists. Line graphs showing change in frequencies of specimens in each of several artifact types were used in the 1910s and 1920s. Seriograms or straight-sided spindles diagraming interpretations of culture change were published in the 1930s, but were seldom subsequently mimicked. Spindle graphs of centered and stacked columns of bars, each column representing a distinct artifact type, each bar the empirically documented relative frequency of specimens in an assemblage, were developed in the 1940s, became popular in the 1950s and 1960s, and are often used to illustrate culture change in textbooks published during the twentieth century. Graphs facilitate visual thinking, different graph types suggest different ontologies and theories of change, and particular techniques of parsing temporally continuous morphological variation of artifacts into types influence graph form. Line graphs, bar graphs, spindle diagrams, and phylogenetic trees of artifacts and cultures indicate archaeologists often mixed elements of Darwinian variational evolutionary change with elements of Midas-touch-like transformational change. Today there is minimal discussion of graph theory or graph grammar in both introductory archaeology textbooks and advanced texts, and elements of the two theories of evolution are often mixed. Culture has changed, and despite archaeology’s unique access to the totality of humankind’s cultural past, there is minimal discussion on graph theory, construction, and decipherment in the archaeological literature.

On the Rising Interdependency between the Power Grid, ICT Network, and E-Mobility: Modeling and Analysis

Boosting critical infrastructures’ (CIs) preparedness to threats, including natural disasters and manmade attacks, is a global imperative. The intrinsic dependencies and interdependencies between CIs hinder their resiliency. Moreover, the evolution of CIs is, in many cases, en routè to tighten those interdependencies. The goal of this paper is to uncover and analyze the rising interdependency between the electric power grid, information and communication technology (ICT) networks, and transportation systems that are heavily reliant on electric-power drivetrains, collectively referred to hereafter as electro-mobility (e-mobility). E-mobility includes electric vehicles (EVs) and electric railway systems. A new influence graph-based model is introduced, as a promising approach to model operational interdependencies between CIs. Each of the links of the influence graph represents the probability of failure of the sink node following a failure of the source node. A futuristic scenario has been analyzed assuming increased dependency of the power grid on ICT for monitoring and control, and high penetration levels of EVs and distributed energy resources (DERs) in an urban region. Inspecting the influence graph shows that the impact of interdependency between the power grid, the ICT network, and the transportation network, for the case study analyzed in this paper, does not lead to failures during normal operation with proper design; however, it is severe during emergency conditions since it leads to failure propagation among the three CIs. This paper sets the stage for more research on this topic, and calls for more attention to interdependency analysis.

Fuzzy Influence Graphs

Trafficking in persons is the most heinous and condemnable organized crime of our time. The eradication of this crime must involve the full cooperation of all. In this paper, we provide a mathematical method to model such cooperation. We use a new concept in fuzzy graph theory, namely, that of incidence. We introduce fuzzy influence graph and characterize influence cutpairs in fuzzy influence graphs since their removal increases the number of connected components of a fuzzy network and thus weakens the potential flow in the network. Some of the properties of influence pairs and influence cutnodes are also studied.