Comparison Analysis of Multipath Routing Implementation in Software Defined Network

Multipath routing is a path search method used as a data transmission process utilizing more than one available path on a network. The multipath routing concept is directed to substitute the single path routing concept for reducing network traffic congestion by distributing data transmission through several available paths. It can be implemented in the Software Defined Network (SDN) environment that separates the control plane and the data plane, which provides flexibility by deploying application-based solutions to resolve the problem. This paper is directed to implement the modified Deep First Search (DFS) multipath routing algorithm and compare the proposed method with Dijkstra and normal DFS multipath algorithm. The contribution was designed by combining the node, edge, path, and bucket weight using port statistics available in OpenFlow standard and manual calculation. The results of the system’s emulation showed that the overall algorithm could determine more than one path for the data transmission process. The average execution time on all algorithms produced 0.0903 ms for the modified DFS multipath algorithm, 0.0858 ms for DFS multipath algorithm, and 0.901 ms Dijkstra multipath algorithm, respectively. The QoS parameter testing results illustrated that the proposed method was better than another multipath routing algorithm in terms of throughput and jitter. However, based on packet loss percentage, the modified method was placed after normal DFS but still generated better results than Dijkstra. Overall, the implement multipath routing concept in SDN with all algorithms could be deployed to provide more than one data transmission path.

The Impact of Reviewers' Creditworthiness on Consumers' Purchase Intention in Edge Path: Implications for the Coronavirus Disease 2019 Pandemic

Reviewers' creditworthiness is an important edge clue in the elaboration likelihood model (ELM). This paper takes the online travel booked by consumers as an example and uses the questionnaire data of 417 outbound passengers from Guangzhou Baiyun International Airport. The paper examines the influence of reviewers' creditworthiness on consumer purchase intentions in the edge path through a mediated moderation model. Investigate the mediating role of conformity behavior can influence the reviewers' creditworthiness on purchase. Thus, it examines the moderating effect of consumer involvement. The results show that the degree of consumer involvement moderates the relationship between reviewers' creditworthiness, and the purchase intention is achieved through the mediation of conformity behavior. The higher the degree of consumer involvement, the less impact the reviewers' creditworthiness has on conformity behavior, and the weaker the positive effects of its purchase intention are found. Implications for the coronavirus disease 2019 (COVID-19) era are also discussed.

A Dual-Path and Lightweight Convolutional Neural Network for High-Resolution Aerial Image Segmentation

Semantic segmentation on high-resolution aerial images plays a significant role in many remote sensing applications. Although the Deep Convolutional Neural Network (DCNN) has shown great performance in this task, it still faces the following two challenges: intra-class heterogeneity and inter-class homogeneity. To overcome these two problems, a novel dual-path DCNN, which contains a spatial path and an edge path, is proposed for high-resolution aerial image segmentation. The spatial path, which combines the multi-level and global context features to encode the local and global information, is used to address the intra-class heterogeneity challenge. For inter-class homogeneity problem, a Holistically-nested Edge Detection (HED)-like edge path is employed to detect the semantic boundaries for the guidance of feature learning. Furthermore, we improve the computational efficiency of the network by employing the backbone of MobileNetV2. We enhance the performance of MobileNetV2 with two modifications: (1) replacing the standard convolution in the last four Bottleneck Residual Blocks (BRBs) with atrous convolution; and (2) removing the convolution stride of 2 in the first layer of BRBs 4 and 6. Experimental results on the ISPRS Vaihingen and Potsdam 2D labeling dataset show that the proposed DCNN achieved real-time inference speed on a single GPU card with better performance, compared with the state-of-the-art baselines.

The Study Trees Potential for Forest in Universitas Lancang Kuning Pekanbaru

The location of the study of Potential tree species for Peripheral Forest Development is determined by Lancang Kuning University, which is on Yos Sudarso road KM 8 Rumbai Pekanbaru, which is a fragmented habitat. The method used is census through measurement of area, structure observation, population enumeration, measurement of three dimensions. Calculating tree species diversity (H '), Assessing the suitability criteria of a type of tree for each type of green edge path area refers to the method of analyzing an integrated research plan, the important value index for the highest vegetation is acacia (Acacia auriculiformis) with a value amounting to 73.11. Acacia (Acacia auriculiformis) type of tree is less suitable. This is because the population is too high, where the debris that falls hard to decompose causes a thick pile when trampled slipperily and endangers safety, which eventually leaves litter accumulation causing soil conditions on the slopes to become soft because it is moist and has the potential to become landslides.

L(2,1)-Edge-Labelings of the Edge-Path-Replacement of a Graph

Two edges [Formula: see text] and [Formula: see text] in a graph [Formula: see text] are said to be adjacent if they have a vertex in common, and distance two apart if they are nonadjacent but both are adjacent to a common edge. An [Formula: see text]-edge-labeling of a graph [Formula: see text] is an assignment of nonnegative integers, called labels, to the edges of [Formula: see text] such that the difference between labels of any two adjacent edges is at least [Formula: see text], and the labels of any two edges that are distance two apart are different. The span of an [Formula: see text]-edge-labeling of a graph [Formula: see text] is the difference between the maximum and minimum labels. The minimum span over all [Formula: see text]-edge-labelings of a graph [Formula: see text] is called the [Formula: see text]-edge-labeling number of [Formula: see text], denoted by [Formula: see text]. For [Formula: see text], the edge-path-replacement of a graph [Formula: see text], denoted by [Formula: see text], is a graph obtained by replacing each edge of [Formula: see text] with a path [Formula: see text] on [Formula: see text] vertices. This paper investigates the [Formula: see text]-edge-labeling number of the edge-path-replacement [Formula: see text] of a graph [Formula: see text] for [Formula: see text]. We get the following main results: [Formula: see text] Let [Formula: see text] be a graph with maximum degree [Formula: see text] and [Formula: see text] be an integer not less than [Formula: see text], then [Formula: see text] if [Formula: see text] is odd, and otherwise [Formula: see text]. [Formula: see text] Let [Formula: see text] be a graph with maximum degree [Formula: see text]. Then [Formula: see text] when [Formula: see text] is even, and [Formula: see text] when [Formula: see text] is odd.