Spanning Tree Research Articles

Multi-Scale Massive Points Fast Clustering Based on Hierarchical Density Spanning Tree

Spatial clustering is dependent on spatial scales. With the widespread use of web maps, a fast clustering method for multi-scale spatial elements has become a new requirement. Therefore, to cluster and display elements rapidly at different spatial scales, we propose a method called Multi-Scale Massive Points Fast Clustering based on Hierarchical Density Spanning Tree. This study refers to the basic principle of Clustering by Fast Search and Find of Density Peaks aggregation algorithm and introduces the concept of a hierarchical density-based spanning tree, combining the spatial scale with the tree links of elements to propose the corresponding pruning strategy, and finally realizes the fast multi-scale clustering of elements. The first experiment proved the time efficiency of the method in obtaining clustering results by the distance-scale adjustment of parameters. Accurate clustering results were also achieved. The second experiment demonstrated the feasibility of the method at the aggregation point element and showed its visual effect. This provides a further explanation for the application of tree-link structures.

Quick balance skill improvement after short-term training with error amplification feedback for older adults

This study investigated behavioral and cortical mechanisms for short-term postural training with error amplification (EA) feedback in the elderly. Thirty-six elderly subjects (65.7 ± 2.2 years) were grouped (control and EA, n = 18) for training in stabilometer balance under visual guidance. During the training session (8 training rounds of 60 s in Day 2), the EA group received visual feedback that magnified errors to twice the real size, whereas the control group received visual feedback that displayed real errors. Scalp EEG and kinematic data of the stabilometer plate and ankle joint were recorded in the pre-test (Day 1) and post-test (Day 3). The EA group (−46.5 ± 4.7%) exhibited greater post-training error reduction than that of the control group (−27.1 ± 4.0%)(p = 0.020), together with a greater decline in kinematic coupling between the stabilometer plate and ankle joint (EA: −26.6 ± 4.8%, control: 2.3 ± 8.6%, p = 0.023). In contrast to the control group, the EA group manifested greater reductions in mean phase-lag index (PLI) connectivity in the theta (4–7 Hz)(p = 0.011) and alpha (8–12 Hz) (p = 0.027) bands. Only the EA group showed post-training declines in the mean PLI in the theta and alpha bands. Minimal spanning tree analysis revealed that EA-based training led to increases in the diameter (p = 0.002) and average eccentricity (p = 0.004) of the theta band for enhanced performance monitoring and reduction in the leaf fraction (p = 0.030) of the alpha band for postural response with enhanced automaticity. In conclusion, short-term EA training optimizes balance skill, favoring multi-segment coordination for the elderly, which is linked to more sophisticated error monitoring with less attentive control over the stabilometer stance.

Hierarchical nearest neighbor descent, in-tree, and clustering

Recently, we have proposed a physically-inspired graph-theoretical method, called the Nearest Descent (ND), which is capable of organizing a dataset into an in-tree graph structure. Due to some beautiful and effective features, the constructed in-tree proves well-suited for data clustering. Although there exist some undesired edges (i.e., the inter-cluster edges) in this in-tree, those edges are usually very distinguishable, in sharp contrast to the cases in the famous Minimal Spanning Tree (MST). Here, we propose another graph-theoretical method, called the Hierarchical Nearest Neighbor Descent (HNND). Like ND, HNND also organizes a dataset into an in-tree, but in a more efficient way. Consequently, HNND-based clustering (HNND-C) is more efficient than ND-based clustering (ND-C) as well. This is well proved by the experimental results on five high-dimensional and large-size mass cytometry datasets. The experimental results also show that HNND-C achieves overall better performance than some state-of-the-art clustering methods.

Epicenter of random epidemic spanning trees on finite graphs

Epidemic source detection is the problem of identifying the network node that originated an epidemic from a partial observation of the epidemic process. The problem finds applications in different contexts, such as detecting the origin of rumors in online social media, and has been studied under various assumptions. Different from prior studies, this work considers an epidemic process on a finite graph that starts on a random node (epidemic source) and terminates when all nodes are infected, yielding a rooted and directed epidemic tree that encodes node infections (i.e., a directed spanning tree of the graph with every edge directed away from the epidemic source). Assuming knowledge of the underlying graph and the undirected spanning tree (i.e., the infection edges but not their directions), can the epidemic source be accurately identified? This work tackles this problem by introducing the epicenter, an efficient estimator for the epidemic source, and thus, the direction of every edge in the epidemic tree. When the underlying graph is vertex-transitive the epicenter can be computed in linear time and it coincides with the well-known distance center of the epidemic tree. Moreover, on a complete graph the epicenter is also the most likely estimator for the source. Finally, the accuracy of the epicenter is evaluated numerically on five different graph models and the performance strongly depends on the graph structure, varying from 31% (on complete graphs) to 13% (on sparse power-law graphs). However, for all graph models considered the epicenter exhibited an accuracy higher than the distance center, being three times more accurate on sparse power-law graphs.

Computing Persistent Homology by Spanning Trees and Critical Simplices.

Topological data analysis can extract effective information from higher-dimensional data. Its mathematical basis is persistent homology. The persistent homology can calculate topological features at different spatiotemporal scales of the dataset, that is, establishing the integrated taxonomic relation among points, lines, and simplices. Here, the simplicial network composed of all-order simplices in a simplicial complex is essential. Because the sequence of nested simplicial subnetworks can be regarded as a discrete Morse function from the simplicial network to real values, a method based on the concept of critical simplices can be developed by searching all-order spanning trees. Employing this new method, not only the Morse function values with the theoretical minimum number of critical simplices can be obtained, but also the Betti numbers and composition of all-order cavities in the simplicial network can be calculated quickly. Finally, this method is used to analyze some examples and compared with other methods, showing its effectiveness and feasibility.

Hybrid Meta-Heuristics Based Task Scheduling Algorithm for Energy Efficiency in Fog Computing

Task scheduling in fog computing is one of the areas where researchers are having challenges as the demand grows for the use of Internet of Things (IoT) to access cloud computing resources. Many resource scheduling and optimization algorithms were used by many researchers in fog computing; some used single techniques while others used combined schemes to achieve dynamic scheduling in fog computing, many optimization techniques are reassessed based on deterministic and meta-heuristics to find out solution to scheduling problem in fog computing. This paper proposes Hybrid Meta-Heuristics Optimization Algorithm (HMOA) for energy efficient task scheduling in fog computing, the study combines Particle Swarm Optimization (PSO) Meta-heuristics and deterministic Spanning Tree (SPT) to achieve task scheduling with the intention of eliminating the drawbacks of the two algorithms when used separately, the PSO is used to schedule user task requests among fog devices, while hybrid MPSO-SPT will be used to perform resource allocation and resource management in the fog computing environment. The study proposed to implement the algorithms using iFogSim in the future work such that performance of the algorithms will be evaluated, assessed and compared with other state of art scheduling and resource management algorithms

Wireless Sensor Security Issues on Data Link Layer: A Survey

A computer network can be defined as many computing devices connected via a communication medium like the internet. Computer network development has proposed how humans and devices communicate today. These networks have improved, facilitated, and made conventional forms of communication easier. However, it has also led to uptick in-network threats and assaults. In 2022, the global market for information technology is expected to reach $170.4 billion. However, in contrast, 95% of cyber security threats globally are caused by human action. These networks may be utilized in several control systems, such as home-automation, chemical and physical assault detection, intrusion detection, and environmental monitoring. The proposed literature review presents a wide range of information on Wireless Social Networks (WSNs) and Internet of Things (IoT) frameworks. The aim is first to be aware of the existing issues (issues with traditional methods) and network attacks on WSN and IoT systems and how to defend them. The second is to review the novel work in the domain and find its limitations. The goal is to identify the area’s primary gray field or current research divide to enable others to address the range. Finally, we concluded that configuration. Message Rapid Spanning Tree Protocol (RSTP) messages have higher efficiency in network performance degradation than alternative Bridge Data Unit Protocol (BPDU) forms. The research divides our future research into solutions and newly developed techniques that can assist in completing the lacking component. In this research, we have selected articles from 2015 to 2021 to provide users with a comprehensive literature overview.

Low-Complexity and High-Coding-Efficiency Image Deletion for Compressed Image Sets in Cloud Servers

Image deletion refers to removing images from a compressed image set in cloud servers, which has always received much attention. However, in some cases images are not successfully deleted, and coding performance still remains to rise. In this paper, we propose a low-complexity and high-coding-efficiency image deletion algorithm. First, all the images are classified into to-be-deleted images, images unneeded to be processed, and images needed to be processed further divided into images needed to be only decoded and images needed to be re-encoded. Then, we also propose a depth- and subtree-constrained minimum spanning tree (DSCMST) heuristics to produce the DSCMST of images needed to be processed. Third, every image unneeded to be processed is added to the just obtained DSCMST as the child of the vertex that is still its parent in the compressed image set. Finally, after the encoding of images needed to be re-encoded, a new compressed image set is constructed, implying the completion of image deletion. Experimental results show that under various circumstances our proposed algorithm can effectively remove any images, including root vertex, internal vertices, and leaf vertices. Moreover, compared with state-of-the-art methods, the proposed algorithm achieves higher coding efficiency while having the minimum complexity.

Multi-Path Minimum Spanning Tree and Superpixel Based Cost Aggregation for Stereo Matching

Multi-Path Minimum Spanning Tree and Superpixel Based Cost Aggregation for Stereo Matching

An 3D MRI Unsupervised Graph-based Skull Stripping Algorithm

Brain tumours and strokes are increasingly common, even from a young age. For this reason preliminary screenings, such as MRI scans, are crucial steps in detecting the abnormalities and finding a treatment. Given the increasing number of people developing different brain problems, there is a need in developing automated segmentation systems. Regardless the various methods presented in the literature, the lack of ground truth images determines the researchers to move towards unsupervised techniques.This paper presents an modified graph-based unsupervised brain segmentation method that uses a minimal spanning tree to segment brain from non-brain tissue. The original method uses human intervention for node selection. The aim is to eliminate human intervention and reduce the computational time required for brain segmentation.The presented method was compared with the original one in two ways: by resizing and scaling the images, and using images without any pre-processing step. Experimental results were obtained on the NFBS dataset and in both experiments the proposed approach delivers better results, both visually and numerically. Performance measures have improved, leading to better overall segmentation. The new method increased the precision and dice coefficient by 20%, resulting in a more accurate segmentation.

Rapid Power Restoration and Network Reconfiguration for Power Distribution System Reliability Enhancement: A Case Study on Bahir Dar Power Distribution Systems

Reliability improvement of power distribution systems is pre-request for economic and social development of any country. Currently, there is rapid growth of industrial, commercial, residential and other service providing institutions. All of these institutions need reliable power supply. Due to this, utilities must work hard and ensure that the customers are getting the reliable power supply. However, higher power interruption frequency and longer interruption duration is the most serious problem facing Ethiopian power distribution systems and this problem affects day to day activity of customers. In this paper, rapid power restoration and network reconfiguration using optimal placement of switching/automating devices is performed. Network reconfiguration and rapid power restoration is performed to improve the reliability problem of Bahir Dar power distribution system. Optimal placement of switching/automating devices for network reconfiguration is determined by Genetic Algorithm (GA) optimization technique using MAT LAB software and rapid power restoration is carried out by Prim’s Algorithm (PA) used for constructing Minimum Spanning Tree (MST) in MAT LAB software. The study is tested at Bata 34-bus feeder of Bahir Dar distribution system. The existing network is assessed and the reliability indices such as SAIFI, SAIDI, ASAI, CAIDI and EENS are evaluated using ETAP 12.6.0 software.

DYNAMICS OF THE MOROCCAN INDUSTRY INDICES NETWORK BEFORE AND DURING THE COVID-19 PANDEMIC

This paper studies the topological properties of the dynamics of the industry indices network at the Moroccan stock exchange by using network theory. The Minimum Spanning Tree (MST) was constructed from the metric distances which had been calculated for the different pairs of industrial indices. The dynamics of the MST were analysed over the period 2013 to 2020 using the sliding window technique. The period studied was divided into the pre-pandemic Covid-19 period and the pandemic Covid-19 period. Connectivity and centrality indicators were calculated to track the connectivity structure over time and to identify the positioning and the importance of the industry indices studied. The result of this study indicates that the network of industry indices was relatively stable during the pre-pandemic Covid-19 period before observing a sudden rapprochement between industries when the Covid-19 pandemic was officially announced. The formation of star-shaped networks was also observed. These networks were centred on the banking industry, essentially during the pandemic Covid-19 period. The banking industry was also positioned at the centre of the Moroccan industry indices network.

Midrapidity cluster formation in heavy-ion collisions

We study the production of clusters and hypernuclei at midrapidity employing the Parton-Hadron- Quantum-Molecular-Dynamics (PHQMD) approach, a microscopic n-body transport model based on the QMD propagation of the baryonic degrees of freedom with density dependent 2-body potential interactions. In PHQMD the cluster formation occurs dynamically, caused by the interactions. The clusters are recognized by the Minimum Spanning Tree (MST) algorithm. We present the PHQMD results for cluster and hypernuclei formation in comparison with the available experimental data at relativistic energies. PHQMD allows to study the time evolution of formed clusters and the origin of their production, which helps to understand how such weakly bound objects are formed and survive in the rather dense and hot environment created in heavy-ion collisions. It offers therefore an explanation of the ’ice in the fire’ puzzle. To investigate whether this explanation of the ’ice in the fire’ puzzle applies only to the MST results we study also the deuterons production by coalescence. We embed MST and coalescence in the PHQMD and UrQMD transport approaches in order to obtain model independent results. We find that both clustering procedures give very similar results for the deuteron observables in the UrQMD as well as in the PHQMD environment. This confirms that our solution for the ’ice in the fire’ puzzle is common to MST and coalescence and independent of the transport approach.

Tree-based Multicast Routing and Channel Assignment for Enhanced Throughout in Emerging Cognitive Radio Networks

In future multi-hop wireless networks like 5G and B5G, efficient large-scale video sharing and data dissemination are expected to rely heavily on multicast routing and Cognitive Radio (CR) technology. While multicast routing is efficient when the network always has access to the spectrum, the dynamic nature of Primary User (PU) activities, heterogeneous spectrum across the CR Network (CRN), and PU access priority make it challenging to implement efficient multicast routing protocols in CRNs. This paper proposes a hierarchical multicast routing mechanism for multi-hop CRNs that exploits the Shortest Path Tree (SPT) and Minimum Spanning Tree (MST) concepts. The proposed multicast routing mechanism consists of tree construction and channel assignment algorithms. The tree-construction algorithm models the network topology as a multicast tree rooted at the CR source and spanning all the CR nodes. Based on the constructed tree, the channel assignment algorithm employs the Probability Of Success (POS) metric to assign channels to the various layers defined by the constructed SPT or MST, ensuring that the most reliable channel is used for the multi-hop multicast transmissions. Simulation experiments are conducted to evaluate the mechanism’s effectiveness, revealing significant improvements in throughput and Packet Delivery Rate (PDR) compared to state-of-the-art protocols under different network conditions. The simulations also show that the SPT-based mechanism outperforms the MST-based mechanism in terms of throughput but has a higher tree construction complexity.

Optimizing the University Student Advising Process through Weighted Minimum Spanning Tree Algorithm

Optimizing the University Student Advising Process through Weighted Minimum Spanning Tree Algorithm

디지털 트윈과 메타버스 지식구조 탐색

본 연구는 디지털 전환의 주요 기술인 디지털 트윈과 메타버스의 지식구조를 탐색하는 것을 주 목적으로 수행되었다. 학술논문은 대표적인 강건한 형식지 중 하나이므로 본 연구에서는 디지털 트윈과 메타버스 지식구조 탐색을 위해 WoS (Web of Science) 데이터베이스에서 학술논문 데이터를 수집하였고, 저자 키워드의 동시등장 관계를 통해 co-word network를 구축하였다. 구축된 co-word network에서 중심성 분석을 통해 주요 노드를 식별하고, 최대 신장트리(maximum spanning tree)를 추출한 후, 군집분석(clustering analysis)를 수행함으로써 디지털 트윈과 메타버스의 지식구조를 제시하였다. 그 결과 현재 지식구조 상으로는 디지털 트윈과 메타버스 사이에는 강한 직접 연계는 없는 것으로 파악되었으며, 메타버스 기술은 교육 영역에서 선제적 적용이 이루어지고 있는 것으로 분석되었다. 본 연구는 디지털 전환의 핵심 기술인 디지털 트윈과 메타버스에 대해 정량적 데이터를 기반으로 지식구조를 파악함으로써 관련 방향성을 제시한 점에서 그 의의가 있다.

Comparison on Routing Processing Performance between Shortest Paths Tree (SPT) and Minimum Spanning Tree (MST)

This study’s objective is to assess the performance of routing processing between Shortest Paths Tree (SPT) and Minimum Spanning Tree (MST). In this paper we propose and create software to calculate the efficiency path of routing processing in the network. Extensive simulations are being carried out with two different algorithms specific for Dijkistra’s algorithm and Kruskal’s algorithm. The network loads in the performance plots are characterized by two important parameters (a number of vertices) - 7 nodes and 10 nodes, more vertices could show good performance in the simulation results and showed that the performance of Kruskal’s algorithm is better than Dijkistra’s algorithm when the network loads are increased from 7 nodes to 10 nodes. In this simulation, Because of the total cost of distance matrix for Kruskal is less than Dijkistra when the number of routers or nodes is increased from 7 nodes to 10 nodes. This software namely Network Aids Design (NAD), is useful for calculation about the performance of routing processing in the network.

Optimization of Gas Distribution in Istanbul Using Minimum Spanning Tree with Probabilistic Approach

The minimum spanning tree (MST) is a method that aims to find the minimum distance for connecting all vertices or nodes in a network of cities or any other centers. For finding the minimum distance that connects all nodes, two different types of algorithms have been commonly used: Kruskal and Prim algorithms. Our objective in this research is to find the most cost-efficient process for linking the natural gas pipeline’s pathways in Istanbul using minimum spanning tree with randomness or uncertainty in the distances between different districts, which are represented by vertices. The goal is to minimize total pipeline distance which connects all districts in metropolitan area of Istanbul. The method proved to be very useful in solving this problem.

Hierarchical risk parity using security selection based on peripheral assets of correlation-based minimum spanning trees

This study proposes hierarchical risk parity portfolios using a new correlation matrix and security selection. We suggest a global motion subtracted correlation matrix, which eliminates the global motion in the cross-correlation matrix. Also, we suggest utilizing the peripheral assets of a correlation-based minimum spanning tree for security selection. The proposed portfolio strategies with security selection outperform benchmarks, showing their nature as smart beta strategies. Specifically, the full correlation with a small number and global motion subtracted correlation with a relatively large number of selected assets exhibit decent performances during the post-crisis bull markets and crisis-induced bear markets, respectively.

Antimicrobial resistance and molecular typing of Salmonella in the chicken production chain in Hubei Province, China

Salmonella is a significant foodborne zoonotic pathogen that endangers both human and animal health. The goal of this research is to gain a preliminary understanding of Salmonella contamination and antimicrobial resistance in the chicken production chain in Hubei Province, China. 1149 animal and environmental samples were collected from chicken farms, slaughterhouses, and retail markets in six cities across Hubei Province in China from 2019 to 2020, yielding Salmonella isolation rates of 4.68% (28/598), 12.21% (47/385), and 9.64% (16/166), respectively. Seventeen distinct serotypes were detected among 53 non-clonal Salmonella strains, of which Meleagridis (26.42%, 14/53) was the dominant serotype. Almost half of the strains (49.06%, 26/53) were multi-drug resistant (MDR). Whole-genome sequencing (WGS) showed that 10 resistance genes (tetA, blaTEM, parC, qnrS1, floR, aac(6′)-Iy, aph(6)-Id, aph(3″)-Ib, aac(6′)-Iaa and sul2) and 7 categories of virulence genes were present in all three links in 22 non-clonal dominant serotype strains. It was shown that Salmonella in the chicken production chain in Hubei Province had a high resistance rate to Tetracycline (TET, 73.58%), Ofloxacin (OFL, 69.81%), Florfenicol (FFC, 60.38%) and Ampicillin (AMP, 39.62%) which was consistent with the widespread use of these drugs in the husbandry industry in China. Salmonella ST types determined by MLST and serotypes determined by WGS had a one-to-one correlation. Minimum spanning tree analysis revealed that there was cross contamination of Salmonella in farms and slaughterhouses, slaughterhouses and markets, animal samples and environmental samples. This work provides useful information for the prevention and control of contamination and antimicrobial resistance of Salmonella in the chicken production chain, as well as demonstrating the dependable role of WGS in Salmonella molecular typing.