Minimum Spanning Tree Research Articles

FlowSOM clustering – A novel pattern recognition approach for water research: Application to a hyper-arid coastal aquifer system

Monitoring and understanding of water resources have become essential in designing effective and sustainable management strategies to overcome the growing water quality challenges. In this context, the utilization of unsupervised learning techniques for evaluating environmental tracers has facilitated the exploration of sources and dynamics of groundwater systems through pattern recognition. However, conventional techniques may overlook spatial and temporal non-linearities present in water research data. This paper introduces the adaptation of FlowSOM, a pioneering approach that combines self-organizing maps (SOM) and minimal spanning trees (MST), with the fast-greedy network clustering algorithm to unravel intricate relationships within multivariate water quality datasets. By capturing connections within the data, this ensemble tool enhances clustering and pattern recognition. Applied to the complex water quality context of the hyper-arid transboundary Caplina/Concordia coastal aquifer system (Peru/Chile), the FlowSOM network and clustering yielded compelling results in pattern recognition of the aquifer salinization. Analyzing 143 groundwater samples across eight variables, including major ions, the approach supports the identification of distinct clusters and connections between them. Three primary sources of salinization were identified: river percolation, slow lateral aquitard recharge, and seawater intrusion. The analysis demonstrated the superiority of FlowSOM clustering over traditional techniques in the case study, producing clusters that align more closely with the actual hydrogeochemical pattern. The outcomes broaden the utilization of multivariate analysis in water research, presenting a comprehensive approach to support the understanding of groundwater systems.

The Budgeted Labeled Minimum Spanning Tree Problem

In order to reduce complexity when designing multi-media communication networks, researchers often consider spanning tree problems defined on edge-labeled graphs. The earliest setting addressed in the literature aims to minimize the number of different media types, i.e., distinct labels, used in the network. Despite being extensively addressed, such a setting completely ignores edge costs. This led to the definition of more realistic versions, where budgets for the total cost, or the number of distinct labels allowed, were introduced. In this paper, we introduce and prove the NP-hardness of the Budgeted Labeled Minimum Spanning Tree problem, consisting in minimizing the cost of a spanning tree while satisfying specified budget constraints for each label type. This problem combines the challenges of cost efficiency and label diversity within a fixed budgetary framework, providing a more realistic and practical approach to network design. We provide three distinct mathematical programming formulations of the problem and design a Lagrangian approach to derive tighter lower bounds for the optimal solution of the problem. The performances of the proposed methods are assessed by conducting a series of computational experiments on a variety of randomly generated instances, which showed how the complexity of the problem increases as the size of the network, as well as the number of labels, increase and the budget restrictions are tightened.

Whole Genome-Sequencing and Phylogenetic Analysis of Bacillus anthracis from 2019-2023 in Ningxia Hui Autonomous Region, China.

Since 2019, the incidence of anthrax in the Ningxia Hui Autonomous Region has increased significantly compared with previous years, so in this situation the anthrax in the Ningxia region not only had a detrimental impact on public health, but also inflicted significant economic repercussions. Therefore, we conducted a molecular epidemiological study of 20 strains from 2019-2023 isolates. This study investigated the origin of Bacillus anthracis and its genetic diversity. We conducted canonical single-nucleotide polymorphisms (CanSNPs) typing and whole genome sequencing based on the extracted nucleic acid of Bacillus anthracis. Based on the whole genome drafts, we studied the genomic characteristics of 20 isolates. Meanwhile, we performed phylogenetic studies based on genome-wide core single-nucleotide polymorphisms (SNPs) using MEGA's Maximum Likelihood (ML) method and core-genome-based multilocus sequence typing (cgMLST) of the core genomes of these strains using BioNumerics' minimum spanning tree (MST) model. The 20 isolates were categorized into sub-lineages A.Br.001/002, and comparative genomic analyses of these strains with other isolates from other parts of the world showed that the strains from Ningxia were correlated with isolates from Europe, Indonesia, Georgia (USA), and Beijing (China). For the 20 isolates in Ningxia, the genetic relationship of the isolates isolated from the same year or region was relatively close. The A.Br.001/002 subgroup was the dominant endemic strain in Ningxia. The genetic relationship and phylogenesis between isolates from Ningxia and strains from Europe and Indonesia suggest that anthrax spread around the globe through ancient trade routes.

The Topological Structure of the Global Foreign Exchange Market During Crises – Comparative Network Analysis

Purpose – The aim of this article was to assess the changes in the topological structure of the currency market caused by two crises: the COVID-19 pandemic in 2020 and Russia’s aggression against Ukraine in 2022. A network of major world currencies was analysed over three six-month sub-periods: the pandemic period 1.02–31.07.2020, the war period 1.02–31.07.2022 and the reference period 1.02–31.07.2021. Research method – We have used the dynamic time warping (DTW) method for comparing time series. DTW distances between pairs of individual currencies were calculated, and, based on them, minimum spanning trees (MST) were constructed, whose topological characteristics were analysed. Results – It turned out that the topological structure of the foreign exchange market varies in the sub-periods studied, and the analysed crises affected the currency network. In addition, the networks generated by the MST depend on the choice of base currency used to measure the value of all other currencies. Originality / value / implications / recommendations – The significance of the results obtained lies in providing a description of the topological structure of the market during the observed crises. The detected hierarchical structures can be useful in theoretical descriptions of currencies and in the search for economic factors affecting specific groups of countries.

An advanced three-dimensional phenotypic measurement approach for extracting Ginkgo root structural parameters based on terrestrial laser scanning.

The phenotyping of plant roots is essential for improving plant productivity and adaptation. However, traditional techniques for assembling root phenotyping information are limited and often labor-intensive, especially for woody plants. In this study, an advanced approach called accurate and detailed quantitative structure model-based (AdQSM-based) root phenotypic measurement (ARPM) was developed to automatically extract phenotypes from Ginkgo tree root systems. The approach involves three-dimensional (3D) reconstruction of the point cloud obtained from terrestrial laser scanning (TLS) to extract key phenotypic parameters, including root diameter (RD), length, surface area, and volume. To evaluate the proposed method, two approaches [minimum spanning tree (MST)-based and triangulated irregular network (TIN)-based] were used to reconstruct the Ginkgo root systems from point clouds, and the number of lateral roots along with RD were extracted and compared with traditional methods. The results indicated that the RD extracted directly from point clouds [coefficient of determination (R 2) = 0.99, root-mean-square error (RMSE) = 0.41cm] outperformed the results of 3D models (MST-based: R 2=0.71, RMSE = 2.20cm; TIN-based: R 2=0.54, RMSE = 2.80cm). Additionally, the MST-based model (F1=0.81) outperformed the TIN-based model (F1=0.80) in detecting the number of first-order and second-order lateral roots. Each phenotyping trait fluctuated with a different cloud parameter (CP), and the CP value of 0.002 (r = 0.94, p < 0.01) was found to be advantageous for better extraction of structural phenotypes. This study has helped with the extraction and quantitative analysis of root phenotypes and enhanced our understanding of the relationship between architectural parameters and corresponding physiological functions of tree roots.

Network analysis of the foreign exchange market using minimum spanning trees constructed from the dynamic time warping distance measure

This paper evaluates the changes that occurred in the topological structure of the foreign exchange market due to the COVID-19 pandemic and after the Russian invasion of Ukraine. A network of 17 currencies was analysed from 1.01.2019 to 31.07.2022 in four sub-periods: before the pandemic, in the year of the pandemic outbreak, in the further course of the pandemic, and the year the war started. Dynamic time warping (DTW) distances between pairs of time series for individual currencies were calculated, and based on these distances, minimum spanning trees (MST) were constructed whose topological characteristics were then analysed. The results show that the topological structure of the foreign exchange market varied in the sub-periods studied. The financial crises caused by the COVID-19 pandemic and the Russian invasion of Ukraine affected the FX network in different ways. However, the two strongest monetary clusters (i.e. clusters centred on the USD and EUR, respectively) have still emerged. Comparing the MSTs for the crisis sub-periods, it can be seen that the USD took a dominant position during the pandemic outbreak, while the EUR played a more significant role during the war. This study contributes to the literature by characterising the topological structure of the FX market in specific sub-periods – during the COVID-19 pandemic and after the Russian invasion of Ukraine. Thus, it represents a particularly relevant study of the impact of crises on the global financial market.

Fusion Data Analysis of the Monitoring Procedure among Ecuadorian Law Professionals using Indeterminate Likert Scales

The study provides a fusion data analysis to investigate the attitudes and perceptions of legal professionals in Ecuador regarding the effectiveness and fairness of the monitoring procedure, using a questionnaire based on indeterminate Likert scales. By employing Triple Refined Indeterminate Neutrosophic Sets and the Minimum Spanning Tree, responses were analyzed to reveal trends and groupings in opinions. The identification of response clusters suggested marked differences and homogeneous subgroups in perspectives, highlighting specific areas within legislation and judicial procedures that require attention. The threshold used for the Minimum Spanning Tree provided a quantitative view of cohesion and discrepancy, which has significant implications for legislative reform and judicial practice. This innovative approach offers a valuable model for future research, with the potential to influence policy-making and the promotion of legislative reforms based on empirical data.

A two-point heuristic to calculate the stepsize in subgradient method with application to a network design problem

We introduce a heuristic rule for calculating the stepsize in the subgradient method for unconstrained convex nonsmooth optimization which, unlike the classic approach, is based on retaining some information from previous iteration. The rule is inspired by the well known two-point stepsize by Barzilai and Borwein (BB) [6] for smooth optimization and it coincides with (BB) in case the function to be minimised is convex quadratic.Under the use of appropriate safeguards we demonstrate that the method terminates at a point that satisfies an approximate optimality condition.The proposed approach is tested in the framework of Lagrangian relaxation for integer linear programming where the Lagrangian dual requires maximization of a concave and nonsmooth (piecewise affine) function. In particular we focus on the relaxation of the Minimum Spanning Tree problem with Conflicting Edge Pairs (MSTC). Comparison with classic subgradient method is presented. The results on some widely used academic test problems are provided too.

Optimizing the Reference Network by Minimum Spanning Tree Approach in SAR Tomography

Optimizing the Reference Network by Minimum Spanning Tree Approach in SAR Tomography

Image Insertion Using Depth- and Topology- Constrained Minimum Spanning Tree for Compressed Image Sets

Image Insertion Using Depth- and Topology- Constrained Minimum Spanning Tree for Compressed Image Sets

MRI-based brain tumor detection and types of classification using CEHJB-RI and JMST

The Brain Tumor (BT), which forms in the brain cells and spreads to the whole brain, may lead to death. Hence, early diagnosis of BT is significant. Still, the detection of BT between the skull and brain region is not concentrated, which results in misclassification outcomes. Thus, this article proposes Magnetic Resonance Imaging (MRI)-based BT detection and types’ classification utilizing Carlitz Exponential Hamilton Jacobi Bellman-based Reinforcement Learning (CEHJB-RL) and JenSorensen similarity-based Minimum Spanning Tree (JMST). Primarily, raw MRI images are taken and then pre-processed. Then, with skull and without skull regions are extracted from the pre-processed image and are subjected to the graph construction. Conversely, the edges are detected from the pre-processed image that can be patch-extracted and subjected to graph construction. By utilizing JMST and Morphological Operations (MO), the graphs are constructed. Thereafter, the features are extracted and fed to the classifier. Then, the type of BT is classified by the classifier using CEHJB-RL. Concerning the performance metrics, the outcomes illustrated that the proposed technique attained a higher accuracy (99.27%), which is better than other existing techniques.

Biotic assemblages of gelatinous zooplankton in the Gulf of Mexico and adjacent waters: An evolutionary biogeographic approach.

Gelatinous zooplankton constitutes a polyphyletic group with a convergent evolutionary history and poorly known biogeographical patterns. In the Gulf of Mexico, a region with complex geological, hydrological, and biotic histories, the study of this group has been limited to taxonomical and ecological aspects. In this study, we implemented a track analysis to identify distributional patterns of gelatinous zooplankton in the Gulf of Mexico and adjacent waters based on a dataset of 6067 occurrence records corresponding to Hydrozoa, Scyphozoa, Cubozoa, Ctenophora, Chaetognatha, Thaliacea, and Appendicularia. Information was compiled from the Global Biodiversity Facility Information (GBIF) and Ocean Biodiversity Information System (OBIS) databases and peer-reviewed literature. Individual tracks were constructed by joining the minimum distance between the occurrence localities of each taxon using a minimum spanning tree algorithm. We identified generalized tracks using parsimony analysis of endemicity with progressive character elimination (PAE-PCE). The areas where different generalized tracks overlapped were considered to represent panbiogeographical nodes. Seven generalized tracks (two with nested patterns) and six panbiogeographical nodes were recognized, mainly in neritic zones. The distributional patterns of gelatinous zooplankton allowed us to identify four biogeographic areas, supporting previously proposed biogeographic schemes. Gelatinous zooplankton in the Gulf of Mexico showed a convergent spatial distribution that can be explained by vicariant and dispersal events. The historical biogeography of the gelatinous biotas of the Gulf of Mexico has been little studied compared to ecological approaches, and the lack of integrative studies considering historical patterns is evident. This type of research is fundamental to understanding the evolutionary history of natural resources from a spatial perspective, identifying sites of biodiversity and endemism, and establishing a biogeographic baseline of the region for further studies.

A methodology for designing biogas pipelines

Dependence on fossil fuels has heightened global energy security issues. Anaerobic digestion (AD) is a technology that can produce biogas to reduce fossil fuel consumption. Transportation of biogas to end users may be achieved using low pressure biogas pipelines. A methodology containing two heuristics (connection based on proximity or output flowrate) is developed to optimise biogas pipeline layouts. A case study of pipeline networks serving up to 10 AD plants and 1 biogas end user is presented. A road network minimum spanning tree (MST) connecting 10 AD plants to the biogas end user is calculated to cost 26% more than a Steiner MST. Multi-criteria decision analysis (MCDA) is used to rank biogas pipeline layouts considering pipeline material cost and biogas flow-rate to the end user. In this analysis, Steiner MSTs always outperform road network MSTs, except when pipeline material cost is disregarded. Scenarios that connected via the proximity heuristic almost always outperformed those which connected via the output heuristic.

SPATIAL PARTITION FOR HETEROGENEOUS CITY NETWORKS COMPOSED OF FACTORS THAT INFLUENCE THE DISTRIBUTION OF THE MACROSCOPIC FUNDAMENTAL DIAGRAM

Using a Macroscopic Fundamental Diagram (MFD) to implement partition control is effective in improving mobility in heterogeneous city networks. As one of the most complex issues in partition control, accurate sub-region partition is critical for control effectiveness. Current partition methods focus on the link density and precondition of existing MFD and disregard the factors that influence MFD distribution. To overcome this drawback, this study uses the characteristic value of the link and the intersection connected to the link as the analysis object and proposes an MFD sub-region partitioning method for large-scale networks. Firstly, the influences of road state parameters on MFD distribution are classified into traffic flow parameters, network physical properties, network operation mechanisms and emergencies. Simulation experiments are conducted to determine the degree to which these classifications affect MFD distribution. Secondly, a partition method combined with the link density and influence parameters of MFD is developed. The method is used for a preliminarily division of a road network through Minimum Spanning Tree (MST) and depth partition by the Normalised Cut (Ncut) algorithm. Finally, a case study is conducted in an actual city centre network, and results show that the developed method is superior to the single method based simply on link density.

Optimization of subsea production facilities layout based on cluster manifold system considering seabed topography

High investment in deep-water development highlights the importance of optimizing the subsea layout, a properly optimized subsea production system reduces costs and contributes to production performance. Aiming at the minimum total construction cost of a subsea cluster manifold system, an integrated mathematical model considering the combination of subsea well grouping, cluster manifold layout, connection relationships, pipe routes and seabed topography is developed in this study. A modified adaptive particle swarm optimization (MAPSO) algorithm coupled with A-star algorithm, clustering algorithm and minimum spanning tree algorithm is used to solve the model by using a newly proposed process. The feasibility and practicality of the model is verified by designing the layout of a deep-water oil field, and the influence of manifold number and seabed topography is demonstrated by the difference in suggested cost. The model provides a new method which can be used to estimate costs more reliably.

Stereo matching algorithm based on improved census transform and minimum spanning tree cost aggregation

Existing stereo matching algorithms suffer from issues such as susceptibility to distortion, weak noise resistance, and a high rate of mismatches in regions with weak textures and discontinuous disparities. To address these challenges, this paper proposes a stereo matching algorithm based on an improved census transform and minimum spanning tree (MST) cost aggregation. In the cost calculation phase, we employ a Gaussian-weighted transformation window and incorporate gradient and edge information to perform weighted fusion of the results. In the cost aggregation process, we introduce a collaborative adaptive window. Each pixel acquires information from the support window of the guided filter (GF) and other pixels within the MST. Furthermore, we integrate the SLIC superpixel segmentation algorithm into MST construction. These two components work synergistically to assign appropriate adaptive weights to pixels, facilitating coordinated cost volume aggregation. Different optimization methods are applied to address mismatched points of various types in post-disparity processing.Performance evaluation using the Middlebury dataset and KITTI dataset demonstrates that our proposed algorithm not only enhances matching accuracy in regions with discontinuous disparities and weak textures but also exhibits significantly improved robustness to interference. Additionally, the resulting disparity map displays smoother edges.

Climate risk performance and returns integration of Chinese listed energy companies

Traditional energy and new energy stock returns are becoming more closely related under the energy transition constraint. In this paper, the core correlation between traditional energy stock returns and new energy stock returns in China is portrayed using the minimum spanning tree (MST) approach and the integration characteristics of energy companies are dynamically analyzed. On this basis, we identify the impact of climate factors on the integration of energy markets by constructing a GARCH-MIDAS model with three dimensions: climate concern, climate physical risk, and climate policy. We find that returns are more likely to be transmitted within traditional energy industries and new energy industries, and firms with the same characteristics tend to form clusters. In a dynamic context, the degree of energy system integration has significant time-varying characteristics and is susceptible to unforeseen events, market policies, and supply-demand relationships. In addition, there is heterogeneity in the effect of climate concern on the integration between traditional energy and new energy markets, and the effects of both climate physical risk and climate policy on the integration of the energy system are significantly negative.

Online distributed waveform-synchronization for acoustic sensor networks with dynamic topology

Acoustic sensing by multiple devices connected in a wireless acoustic sensor network (WASN) creates new opportunities for multichannel signal processing. However, the autonomy of agents in such a network still necessitates the alignment of sensor signals to a common sampling rate. It has been demonstrated that waveform-based estimation of sampling rate offset (SRO) between any node pair can be retrieved from asynchronous signals already exchanged in the network, but connected online operation for network-wide distributed sampling-time synchronization still presents an open research task. This is especially true if the WASN experiences topology changes due to failure or appearance of nodes or connections. In this work, we rely on an online waveform-based closed-loop SRO estimation and compensation unit for nodes pairs. For WASNs hierarchically organized as a directed minimum spanning tree (MST), it is then shown how local synchronization propagates network-wide from the root node to the leaves. Moreover, we propose a network protocol for sustaining an existing network-wide synchronization in case of local topology changes. In doing so, the dynamic WASN maintains the MST topology after reorganization to support continued operation with minimum node distances. Experimental evaluation in a simulated apartment with several rooms proves the ability of our methods to reach and sustain accurate SRO estimation and compensation in dynamic WASNs.

Using Trust Measures to Optimize Neighbor Selection for Smart Blockchain Networks in IoT

The blockchain paradigm is currently modifying all the major Internet of Things (IoT) application domains, giving the opportunity of constructing decentralized environments in which trustful and anonymous activities can be efficiently performed. Blockchain proposes an approach which assures data saved on a distributed ledger would be continuously synchronized and in such a way the ledger will remain consistent. The distributed ledger has the responsibility to maintain a higher level of consistency. Given a connected network, the Optimum Neighbor Selection (ONS) of paths can be obtained by finding the Minimum Spanning Tree of the network. However, none of past approaches proposed in the literature to construct ONS considered the problem of having nodes with low levels of reliability in the network and even malicious or fraudulent nodes, that is a situation very common in an IoT environment. In this paper, we propose an optimized blockchain ONS algorithm, called Trust-based ONS (TONS), which allows the miners to communicate with a globally-optimized selection of neighbors. The algorithm can also guarantee that these nodes are the most reliable miners. We also describe an experimental simulation campaign we have performed to evaluate the effectiveness and the efficiency of our approach.

How does urban heat island differ across urban functional zones? Insights from 2D/3D urban morphology using geospatial big data

The deteriorating urban thermal environment poses a huge impediment to sustainable urban development, which is closely related to the urban morphology under different urban functional zones (UFZs). By integrating remote sensing and geospatial big data, this work aims to reveal the divergent mechanism behind Urban Heat Island (UHI) across UFZs taking insights from 2D/3D urban morphology. The Minimum Spanning Tree (MST) indicator depicting 3D building distribution was introduced and integrated with the classic indicators. Their impacts on UHI were measured by ensemble learning and SHapley Additive exPlanations (SHAP) model. Taking Wuhan as the study area, the eighteen 2D/3D urban morphology indicators affecting UHI in different locations and UFZs were extensively examined and compared. The results reveal that: 1) The impact of 2D/3D urban morphology indicators on UHI significantly varies across different UFZs, with dominance in transportation zones exhibiting opposite polarity compared to the other types; 2) positive impacts have a decreasing trend from the urban center to the edge, while negative impacts exhibit opposite trend; 3) XGBoost outperforms other classic methods in interpreting the impact of urban morphology on UHI for all UFZ types. The findings improving knowledge of UHI across UFZs will provide a practical guide for urban planning.