The flow behavior around a cam shaped tube in a cross flow has been investigated experimentally using flow visualization and pressure distribution measurements. The range of attack angle and Reynolds number based on an equivalent circular diameter are within 0£a£360o and 2×104 Reeq>3.4×104), respectively. The pressure drag features are clarified in relation to the flow behavior around the tube. It is found that the highest pressure drag coefficient occurs at a=90o and 270o over the whole range of Reynolds number. Results show that the pressure drag coefficient of the cam - shaped tube is lower than that of a circular tube with the same surface area for more of the attack angles.

Data visualization is the process of transforming data, information, and knowledge into visual form, making use of humans’ natural visual capabilities which reveals relationships in data sets that are not evident from the raw data, by using mathematical techniques to reduce the number of dimensions in the data set while preserving the relevant inherent properties. In this paper, we formulated data visualization as a Quadric Assignment Problem (QAP), and then presented an Artificial Bee Colony (ABC) to solve the resulted discrete optimization problem. The idea behind this approach is to provide mechanisms based on ABC to overcome trapped in local minima and improving the resulted solutions. To demonstrate the application of ABC on discrete optimization in data visualization, we used a database of electricity load and compared the results to other popular methods such as SOM, MDS and Sammon’s map. The results show that QAP-ABC has high performance with compared others.

Purpose: This study discovers the global knowledge structure of landslide research over the past twenty years in Researchers' research outputs.Methodology: This study uses co-word analysis to determine and visualize the global structure of landslide knowledge, track the most widely used subjects, and choose the conceptual dynamics in research on landslides.  This scientometric method was used to study data from the Web of Science Core Collection (WOSCC) regarding 9185 landslide research publications produced from 2000 to 2020.Findings: The keyword “geographic information systems (GIS)” had the highest frequency, and the phrase “landslide susceptibility” was the most often used co-word pair. Co-word analysis produced 15 thematic clusters. Cluster 8 has the highest density among the clusters, and the highest centrality was seen in Cluster 1. Most of the thematic clusters are located in the third quadrant of the strategic diagram, indicating either emerging or declining clusters. The maturity and cohesion of each cluster show their trends. Conclusion: Co-word analysis, as a suitable and powerful tool, can visualize the scientific and intellectual structure of landslides and track the most used topics, and determine the conceptual dynamics and areas of Landslide research, and its results will be of great help to research and practical planners and policymakers. The results of the study significantly help researchers and applied planners and policymakers in adopting appropriate measures to reach more effective solutions in the shortest possible time. Also, develop research evaluation policies, and university managers aimed to create research evaluation policies.Value:The research has also explored the complex relationships governing international studies, illuminates angles, highlights research gaps, and leads scholars and analyses. These results will pave the paths forward for planners and policymakers in organizations and centers actively addressing landslide management’s strategic plans. Subsequent studies could focus on the examination of the networks of collaboration between centers and researchers, and their impacts on decision-making.