Developing the hydrological dependency structure between streamgage and reservoir networks

Reliable operation of physical infrastructures such as reservoirs, dikes, nuclear power plants positioned along a river network depends on monitoring riverine conditions and infrastructure interdependency with the river network, especially during hydrologic extremes. Developing this cascading interdependency between the riverine conditions and infrastructures for a large watershed is challenging, as conventional tools (e.g., watershed delineation) do not provide the relative topographic information on infrastructures along the river network. Here, we present a generic geo-processing tool that systematically combines three geospatial layers: topographic information from the National Hydrographic Dataset (NHDPlusV2), streamgages from the USGS National Water Information System, and reservoirs from the National Inventory of Dams, to develop the interdependency between reservoirs and streamgages along the river network for upper and lower Colorado River Basin (CRB) resulting in River and Infrastructure Connectivity Network (RICON) that shows the said interdependency as a concise edge list for the CRB. Another contribution of this study is an algorithm for developing the cascading interdependency between infrastructure and riverine networks to support their management and operation.

(1,0)-Relaxed strong edge list coloring of planar graphs with girth 6

Let [Formula: see text] be a graph. An [Formula: see text]-relaxed strong edge [Formula: see text]-coloring is a mapping [Formula: see text] such that for any edge [Formula: see text], there are at most [Formula: see text] edges adjacent to [Formula: see text] and [Formula: see text] edges which are distance two apart from [Formula: see text] assigned the same color as [Formula: see text]. The [Formula: see text]-relaxed strong chromatic index, denoted by [Formula: see text], is the minimum number [Formula: see text] of an [Formula: see text]-relaxed strong [Formula: see text]-edge-coloring admitted by [Formula: see text]. [Formula: see text] is called [Formula: see text]-relaxed strong edge [Formula: see text]-colorable if for a given list assignment [Formula: see text], there exists an [Formula: see text]-relaxed strong edge coloring [Formula: see text] of [Formula: see text] such that [Formula: see text] for all [Formula: see text]. If [Formula: see text] is [Formula: see text]-relaxed strong edge [Formula: see text]-colorable for any list assignment with [Formula: see text] for all [Formula: see text], then [Formula: see text] is said to be [Formula: see text]-relaxed strong edge [Formula: see text]-choosable. The [Formula: see text]-relaxed strong list chromatic index, denoted by [Formula: see text], is defined to be the smallest integer [Formula: see text] such that [Formula: see text] is [Formula: see text]-relaxed strong edge [Formula: see text]-choosable. In this paper, we prove that every planar graph [Formula: see text] with girth 6 satisfies that [Formula: see text]. This strengthens a result which says that every planar graph [Formula: see text] with girth 7 and [Formula: see text] satisfies that [Formula: see text].

Analysing Big Social Graphs Using a List-based Graph Folding Algorithm

In this paper, we explore the ways to represent big social graphs using adjacency lists and edge lists. Furthermore, we describe a list-based algorithm for graph folding that makes possible to analyze conditionally infinite social graphs on resource constrained mobile devices. The steps of the algorithm are (a) to partition, in a certain way, the graph into clusters of different levels, (b) to represent each cluster of the graph as an edge list, and (c) to absorb the current cluster by the cluster of the next level. The proposed algorithm is illustrated by the example of a sparse social graph.

Using cluster edge counting to aggregate iterations of centroid-linkage clustering results and avoid large distance matrices

Sequence clustering is a fundamental tool of molecular biology that is being challenged by increasing dataset sizes from high-throughput sequencing. The agglomerative algorithms that have been relied upon for their accuracy require the construction of computationally costly distance matrices which can overwhelm basic research personal computers. Alternative algorithms exist, such as centroid-linkage, to circumvent large memory requirements but their results are often input-order dependent. We present a method for bootstrapping the results of many centroid-linkage clustering iterations into an aggregate set of clusters, increasing cluster accuracy without a distance matrix. This method ranks cluster edges by conservation across iterations and reconstructs aggregate clusters from the resulting ranked edge list, pruning out low-frequency cluster edges that may have been a result of a specific sequence input order. Aggregating centroid-linkage clustering iterations can help researchers using basic research personal computers acquire more reliable clustering results without increasing memory resources.

Minimum Spanning Tree Dynamic Demonstration System Implementation

The graph vertices design into classes, for each vertex in the design of the abscissa, ordinate and in-degree members, realizes the dynamic demonstration minimum spanning tree. Dynamic visualize Prime algorithm and kruskal algorithm implementation process. Around two window synchronization of animation, " in order to find the minimum edge " list box list the minimum edge of a minimum spanning tree ,with thick line in the left window drawing the found minimum edge and On the edge of the vertex, in the right box demo the process of algorithm dynamic execution.

Graph Coloring

In this chapter a particular type of graph labeling, called graph coloring, is introduced and discussed. In the first part, the simple type of coloring, vertex coloring, is focused. Thus, concerning vertex coloring, some terms and definitions are introduced. Next, some theorems and applying those theorems, some coloring algorithms and applications are introduced. At last, some helpful concepts such as critical graphs, list coloring, and vertex decomposition are presented and discussed. In the second section, edge coloring is focused. Thus, concerning edge coloring, some terms and definitions are described, some important information about edge chromatic number and edge list coloring is presented, and applying them, classification of graphs using the coloring approach is summarized. At last some helpful concepts such as edge list coloring and edge decomposition are illustrated and discussed.

HANDLING EDGE LISTS IN 2D VECTOR GRAPHICS HARDWARE

In rendering two-dimensional (2D) vector graphics, edge lists are often so large that their handling hinders the desired operation of portable devices. This paper proposes and evaluates an efficient edge-list handling method for a 2D vector graphics hardware accelerator. The proposed method selects edges that span the next scanline from among those spanning the current scanline and stores them in a small list in the internal memory. An edge list is assigned to each scanline and it stores only those edges that have not appeared in previous edge lists. Given that most active edges span only a few scanlines, the internal list can be small and implemented in the accelerator, whereas the edge lists are held in the external memory. Experimental results show that the proposed method can reduce external memory access by 23.4%–76.6% for the benchmark images considered compared to the prior methods.