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Symptomatic partial-thickness rotator cuff tears and full-thickness tears with poor tissue quality often pose a dilemma for orthopaedic surgeons. Despite advances in repair techniques and fixation devices, retear rates remain high. Progression of partial-thickness tears has been noted to be over 50%, with remaining fibers seeing increased strain. Patch augmentation that induces a healing response while decreasing peak strain of adjacent tissue is becoming more popular among orthopaedic surgeons. Therefore, we present an all-arthroscopic technique guide for application of a Food and Drug Administrationeapproved bovine bioinductive patch (Rotation Medical, Plymouth, MN).T he incidence of partial-thickness rotator cuff tears has been shown to be between 4% and 26% depending on the age of the patient. 1 Most of these tears are articular sided and have poor healing potential because of hypovascularity and decreased tensile strength. Unfortunately, partial tendon lesions are often much more painful than full-thickness tears, showing higher levels of pain mediators such as substance P. 2

Many real-world spatial systems can be conceptualized as networks. In these conceptualizations, nodes and links represent system components and their interactions, respectively. Traditional network analysis applies graph theory measures to static network datasets. However, recent interest lies in the representation and analysis of evolving networks. Existing network automata approaches simulate evolving network structures, but do not consider the representation of evolving networks embedded in geographic space nor integrating actual geospatial data. Therefore, the objective of this study is to integrate network automata with geographic information systems (GIS) to develop a novel modelling framework, Geographic Network Automata (GNA), for representing and analyzing complex dynamic spatial systems as evolving geospatial networks. The GNA framework is implemented and presented for two case studies including a spatial network representation of (1) Conway’s Game of Life model and (2) Schelling’s model of segregation. The simulated evolving spatial network structures are measured using graph theory. Obtained results demonstrate that the integration of concepts from geographic information science, complex systems, and network theory offers new means to represent and analyze complex spatial systems. The presented GNA modelling framework is both general and flexible, useful for modelling a variety of real geospatial phenomena and characterizing and exploring network structure, dynamics, and evolution of real spatial systems. The proposed GNA modelling framework fits within the larger framework of geographic automata systems (GAS) alongside cellular automata and agent-based modelling.

Agent-based models (ABM) are used to represent a variety of complex systems by simulating the local interactions between system components from which observable spatial patterns at the system-level emerge. Thus, the degree to which these interactions are represented correctly must be evaluated. Networks can be used to discretely represent and quantify interactions between system components and the emergent system structure. Therefore, the main objective of this study is to develop and implement a novel validation approach called the NEtworks for ABM Testing (NEAT) that integrates geographic information science, ABM approaches, and spatial network representations to simulate complex systems as measurable and dynamic spatial networks. The simulated spatial network structures are measured using graph theory and compared with empirical regularities of observed real networks. The approach is implemented to validate a theoretical ABM representing the spread of influenza in the City of Vancouver, Canada. Results demonstrate that the NEAT approach can validate whether the internal model processes are represented realistically, thus better enabling the use of ABMs in decision-making processes. ARTICLE HISTORY

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