Coopetition Bunch Graphs: Competition and Cooperation on COVID19 Research

We introduce bunch graphs to generalize the structure of graphs, where bunches (groups) are considered essential. The significance of a bunch (a node in a network) is to represent a group as a single entity. We find that simultaneous competition and collaboration are observed among individuals /groups working on a topic/project in the real world. This study captures the notion of simultaneity of competition and collaboration among different species/ communities/ individuals using coopetition bunch graphs. We also introduce two measures for assessing coopetition in a network: a) coopetition number and b) coopetition degree of a node in a bunch graph to capture the competition and collaboration of the node in the network. We identify two types of coopetitions from practice: strong-form coopetition and weak-form coopetition, and corresponding coopetition indices to measure each form of coopetition. We find real-world examples to illustrate our approach and computations for strong-form and weak-form coopetition. The current world is witnessing a global pandemic due to COVID19. The scientists from different research institutes are engaged in overcoming the situation. This study demonstrates that countries and institutes pursue pure competition, collaboration, and coopetition for a variety of reasons (innovation, costs, strategic, and tactical reasons). We compare our approach with that based on existing techniques of semidirected graphs and find that the results of the two approaches are significantly different, largely due to bunch effects. We illustrate this fact at node, sub-graph, and graph levels.

Concepts on Coloring of Cluster Hypergraphs with Application

Coloring of graph theory is widely used in different fields like the map coloring, traffic light problems, etc. Hypergraphs are an extension of graph theory where edges contain single or multiple vertices. This study analyzes cluster hypergraphs where cluster vertices too contain simple vertices. Coloring of cluster networks where composite/cluster vertices exist is done using the concept of coloring of cluster hypergraphs. Proper coloring and strong coloring of cluster hypergraphs have been defined. Along with these, local coloring in cluster hypergraphs is also provided. Such a cluster network, COVID19 affected network, is assumed and colored to visualize the affected regions properly. [ABSTRACT FROM AUTHOR] Copyright of Mathematical Problems in Engineering is the property of Hindawi Limited and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Sub-basin prioritization for assessment of soil erosion susceptibility in Kangsabati, a plateau basin: A comparison between MCDM and SWAT models.

Kangsabati basin located in tropical plateau region faces multiple problems of soil erosion susceptibility (SES), soil fertility deterioration, and sedimentation in reservoirs. Hence, identification of SES zones in thirty-eight sub-basins (SB) for basin prioritization is necessary. The present research addressed the issue by using four multi-criteria decision-making (MCDM) models: VlseKriterijumska optimizacija I Kompromisno Resenje (VIKOR), technique for order preference by similarity to ideal solution (TOPSIS), simple additive weighing (SAW), compound factor (CF). To determine the best fitted method from MCDM for erosion susceptibility (ES), a comparison has been made with Soil and Water Assessment Tool (SWAT), where fifteen morphometric parameters were considered for MCDM, and meteorological data, soil, slope and land use land cover (LULC) were considered for SWAT model. Two validation indices of percentage change and intensity change were used for evaluation and comparison of MCDM results. With SWAT model performance, SWAT calibration and uncertainty analysis programs (CUP) was used for sensitive analysis of SWAT parameters on flow discharge and sediment load simulation. The results showed that 23, 16, 18 SB have high ES; therefore they were given 1 to 3 ranks, whereas 31, 37, 21SB have low ES, hence given 38 to 36 rank as predicted by MCDM methods and SWAT. MCDM validation results depict that VIKOR and CF methods are more acceptable than TOPSIS and SAW. Calibration (flow discharge R2 0.86, NSE 0.75; sediment load R2 0.87, NSE 0.69) and validation (flow discharge R2 0.79, NSE 0.55; sediment load R2 0.79, NSE 0.76) of SWAT model indicated that simulated results are well fitted with observed data. Therefore, VIKOR reflects the significant role of morphometric parameters on ES, whereas SWAT reflects the significant role of LULC, slope, and soil on ES. However, it could be concluded that VIKOR is more effective MCDM method in comparison to SWAT prediction.