Network Path Research Articles

A modified scenario bundling method for shortest path network interdiction under endogenous uncertainty

A modified scenario bundling method for shortest path network interdiction under endogenous uncertainty

Multilateral collaborative governance of Arctic shipping safety: Examining the impact of the Arctic Council via collaborative network analysis

Global climate change has dramatically reduced Arctic sea ice, opening new potential shipping routes, which has garnered international attention. This study explores the role of the Arctic Council in the collaborative governance network focused on Arctic shipping safety. For this purpose, a collection of 49 collaborative events initiated by international organisations and the Arctic Council was used as the data sample. These events are divided into two groups, one containing all the events and the other excluding those initiated by the Arctic Council. As a result, two collaborative networks are developed and evaluated based on network density, average path length, network transitivity, centrality indices (degree, closeness, betweenness), and clustering analysis. The results demonstrate that the Arctic Council significantly enhances the network density and transitivity of the international collaboration network, and reshapes core-periphery dynamics and cohesive subgroup structures. For instance, under the influence of the Arctic Council, Canada, France, and Japan surpass the United Kingdom in centrality (degree, closeness, betweenness). Results of the clustering coefficients reveal the Arctic Council's impact on connections among states, leading to shifts in collaboration patterns. Additionally, subgroup cohesion and density coefficients confirm the Arctic Council's role in promoting collaboration among states and restructuring governance networks.

Brain network changes after the first seizure: an insight into medication response?

After a first epileptic seizure, anti-seizure medications (ASMs) can change the likelihood of having a further event. This prospective study aimed to quantify brain network changes associated with taking ASM monotherapy. We applied graph theoretical network analysis to longitudinal resting-state functional MRI (fMRI) data from 28 participants who had recently experienced their first seizure. Participants were imaged before and during long-term ASM therapy, with a mean inter-scan interval of 6.9 months. After commencing ASM, we observed an increase in the clustering coefficient and a decrease in network path length. Brain changes after ASM treatment were most prominent in the superior frontoparietal and inferior fronto-temporal regions. Participants with recurrent seizures display the most pronounced network changes after ASM treatment. This study shows changes in brain network function after ASM administration, particularly in participants with recurrent seizures. Larger studies that ideally include control cohorts are required to understand further the connection between ASM-related brain network changes and longer-term seizure status.

Trajectory of antibiotic resistome response to antibiotics gradients: A comparative study from pharmaceutical and associated wastewater treatment plants to receiving river

Pharmaceutical wastewater often contains significant levels of antibiotic residues, which continuously induce and promote antibiotic resistance during the sewage treatment process. However, the specific impact of antibiotics on the emergence of antibiotic resistance genes (ARGs), microbiomes, and mobile genetic elements (MGEs), as well as the dose-response relationship remain unclear. Herein, through metagenomic sequencing and analysis, we investigated the fate, transmission, and associated risk of ARGs over a ten-year period of exposure to a gradient of sulfonamide antibiotics at a pharmaceutical wastewater treatment plant (PWWTP), an associated wastewater treatment plant (WWTP), and the receiving river. Through abundance comparison and principal co-ordinates analysis (PCoA), our results revealed distinct ARG, microbiome, and MGE profiles across different antibiotic concentrations. Notably, there was a decreasing trend in the abundance of ARGs and MGEs as the antibiotic concentrations were attenuated (p < 0.05). Further partial least squares path modeling analysis, Procrustes analysis and network analysis indicated that variation in MGEs and microbiomes were the driving forces behind the distribution of ARGs. Based on these findings, we proposed an antibiotic-microbiome-MGE-ARG dissemination paradigm, in which integrons as key drivers were closely associated with prevalent ARGs such as sul1, sul2, and aadA. With a focus on human pathogenic bacteria and the associated health risks of ARGs, we conducted pathogen source analysis and calculated the antibiotic resistome risk index (ARRI). Our findings highlighted potential risks associated with the transition from PWWTP to WWTP, raising concerns regarding risk amplification due to the mixed treatment of antibiotic-laden industrial wastewater and domestic sewage. Overall, the results of our study provide valuable information for optimizing wastewater treatment practices to better manage antibiotic resistance.

Minmax regret 1-sink location problems on dynamic flow path networks with parametric weights

Minmax regret 1-sink location problems on dynamic flow path networks with parametric weights

On non-superperfection of edge intersection graphs of paths

The routing and spectrum assignment problem in modern flexgrid elastic optical networks asks for assigning to given demands a route in an optical network and a channel within an optical frequency spectrum so that the channels of two demands are disjoint whenever their routes share a link in the optical network. This problem can be modeled in two phases: firstly, a selection of paths in the network and, secondly, an interval coloring problem in the edge intersection graph of these paths. The interval chromatic number equals the smallest size of a spectrum such that a proper interval coloring is possible, the weighted clique number is a natural lower bound. Graphs where both parameters coincide for all possible non-negative integral weights are called superperfect. Therefore, the occurrence of non-superperfect edge intersection graphs of routing paths can provoke the need of larger spectral resources. In this work, we examine the question which minimal non-superperfect graphs can occur in the edge intersection graphs of routing paths in different underlying networks: when the network is a path, a tree, a cycle, or a sparse planar graph with small maximum degree. We show that for any possible network (even if it is restricted to a path) the resulting edge intersection graphs are not necessarily superperfect. We close with a discussion of possible consequences and of some lines of future research.

PathEL: A novel collective entity linking method based on relationship paths in heterogeneous information networks

Collective entity linking always outperforms independent entity linking because it considers the interdependencies among entities. However, the existing collective entity linking methods often have high time complexity, do not fully utilize the relationship information in heterogeneous information networks (HIN) and most of them are largely dependent on the special features associated with Wikipedia. Based on the above problems, this paper proposes a novel collective entity linking method based on relationship path in heterogeneous information networks (PathEL). The PathEL classifies complex relationships in HIN into 1-hop paths and 3 types of 2-hop paths, and measures entity correlation by the path information among entities, ultimately combining textual semantic information to realize collective entity linking. In addition, facing the high complexity of collective entity linking, this paper proposes to solve the problem by combining the variable sliding window data processing method and the two-step pruning strategy. The variable sliding window data processing method limits the number of entity mentions in each window and the pruning strategy reduces the number of candidate entities. Finally, the experimental results of three benchmark datasets verify that the model proposed in this paper performs better in entity linking than the baseline models. On the AIDA CoNLL dataset, compared to the second-ranked model, our model has improved P, R, and F1 scores by 1.61%, 1.54%, and 1.57%, respectively.

A Study on the Cascade Evolution Mechanism of Construction Workers’ Unsafe Behavior Risk Factors

There are numerous risk factors across various dimensions that lead to unsafe behaviors among construction workers, and the interactions between these factors are complex and intertwined. Therefore, it is crucial to comprehensively explore the mechanisms of these risk factors across all dimensions to reduce the accident rate. This paper combines cascading failure and entropy flow models to construct a cascading trigger model for identifying key nodes and paths in a risk network. First, this paper identifies the risk factors in the individual, organizational, managerial, and environmental dimensions, dividing them into deep and surface factors. Based on this, a risk network is constructed, and cascading failure is introduced to simulate the dynamic evolution of risks. Then, the entropy flow model is introduced to quantify the risk flow in risk propagation. Finally, to address the uncertainty of risk occurrence, Visual Studio Code is used for coding, and a simulation platform is built using JavaScript. After conducting simulation experiments, the results are statistically analyzed. The results show that the key nodes of deep factors are mainly concentrated in the individual dimension (herd mentality, negative emotions, physical fatigue, fluke mindset), organizational dimension (poor cohesion, poor internal communication), and managerial dimension (abusive leadership style and insufficient/low-quality safety education and training); the surface factors are mainly the poor safety climate in the organizational dimension. The findings provide theoretical support for reducing the accident rate caused by unsafe worker behaviors, aiming to reduce accident risk losses by cutting off risk propagation paths.

Chemography-guided analysis of a reaction path network for ethylene hydrogenation with a model Wilkinson's catalyst.

Visualization and analysis of large chemical reaction networks become rather challenging when conventional graph-based approaches are used. As an alternative, we propose to use the chemical cartography ("chemography") approach, describing the data distribution on a 2-dimensional map. Here, the Generative Topographic Mapping (GTM) algorithm - an advanced chemography approach - has been applied to visualize the reaction path network of a simplified Wilkinson's catalyst-catalyzed hydrogenation containing some 105 structures generated with the help of the Artificial Force Induced Reaction (AFIR) method using either Density Functional Theory or Neural Network Potential (NNP) for potential energy surface calculations. Using new atoms permutation invariant 3D descriptors for structure encoding, we've demonstrated that GTM possesses the abilities to cluster structures that share the same 2D representation, to visualize potential energy surface, to provide an insight on the reaction path exploration as a function of time and to compare reaction path networks obtained with different methods of energy assessment.

Optimization of UAV Flight Paths in Multi-UAV Networks for Efficient Data Collection

Optimization of UAV Flight Paths in Multi-UAV Networks for Efficient Data Collection

Water and Energy Conservation across Provinces and Sectors in China: Based on a Complex Network Perspective

Previous studies on the water–energy nexus mainly focused on the calculation and comparison of resource consumption at the national or regional level, lacking interprovincial sector-sector transfer analysis. In this study, the intensity of water and energy consumption of various sectors in China was calculated, the key nodes and paths of resource networks were identified, and countermeasures for resource conservation were proposed from the new perspective of the “dual saving” and “bidirectional saving” of water and energy. The results showed that the metallurgical industry (Me) in Jiangsu and the chemical industry (Ch) in Hebei and Jiangsu had high node strength in the water and energy network and were key sectors in China with “dual saving” effects of water and energy. The construction industry, Ch, Me in Jiangsu, electricity and hot water production and supply industry in Beijing, and Me in Hebei had high node strength in the water-related energy network and energy-related water network, significantly supporting the “bidirectional saving” effect of water and energy in China. The electrical equipment industry (El) in Jiangsu → El in Zhejiang, El in Zhejiang → El in Shanghai frequently appeared in key paths, which could effectively reduce the resource consumption of the entire network.

Path optimization algorithm for mobile sink in wireless sensor network

In order to alleviate the limitation of mobile sink in Wireless Sensor Network (WSN) by environmental and power problems, this paper proposes a new signal collection method for network communication in wireless sensor network with the goal of shortening the path of the mobile sink, and then proposes the ACO-TU (Ant Colony Optimization-Team Up) algorithm based on ant colony algorithm by adding concepts such as node pheromone. The algorithm groups the mobile units of the ant colony algorithm and further optimizes the sink path in the wireless sensor network by specific mobile strategies. Experimental results show that the method of sink moving path optimization proposed has a significant improvement in reducing power consumption and distance shortening, which can further increase the service life of the mobile sink and improve the quality of signal transmission. The results of comparison with the most recent relevant algorithms show that the ACO-TU algorithm has the best optimization performance, which proves the effectiveness of the algorithm.

FDiff-Fusion: Denoising diffusion fusion network based on fuzzy learning for 3D medical image segmentation

In recent years, the denoising diffusion model has achieved remarkable success in image segmentation modeling. With its powerful nonlinear modeling capabilities and superior generalization performance, denoising diffusion models have gradually been applied to medical image segmentation tasks, bringing new perspectives and methods to this field. However, existing methods overlook the uncertainty of segmentation boundaries and the fuzziness of regions, resulting in the instability and inaccuracy of the segmentation results. To solve this problem, a denoising diffusion fusion network based on fuzzy learning for 3D medical image segmentation (FDiff-Fusion) is proposed in this paper. By integrating the denoising diffusion model into the classical U-Net network, this model can effectively extract rich semantic information from input medical images, thus providing excellent pixel-level representation for medical image segmentation. In this paper, a fuzzy learning module is designed on the skip path of U-Net network because of the widespread boundary uncertainty and region blurring of medical image segmentation. The module sets several fuzzy membership functions for the input encoded features to describe the similarity degree between the feature points, and applies fuzzy rules to the fuzzy membership functions, thus enhancing the modeling ability of the model for uncertain boundaries and fuzzy regions. In addition, in order to improve the accuracy and robustness of the model segmentation results, we introduced an iterative attention feature fusion method in the test phase, which added local context information to the global context information in the attention module to fuse the prediction results of each denoising time step. Finally, to validate the effectiveness of FDiff-Fusion, we compare it with existing advanced segmentation networks on the BRATS 2020 brain tumor dataset and the BTCV abdominal multi-organ dataset. The results show that FDiff-Fusion significantly improves the Dice scores and HD95 distance on these two datasets, demonstrating its superiority in medical image segmentation tasks.

Analysis of passenger transportation and the public transportation impact on the reduction in a smart city

The state of atmospheric pollution is determined by the growth of the population, the amount of transport and the generated volumes of emissions. The object is the process of analyzing passenger transportation in the city. The subject is passenger transport analysis methods. Purpose: analysis of passenger transportation and approaches to optimization of public transport based on the concept of a smart city. Tasks: analysis of passenger transportation, classification of existing conceptual approaches to optimization of public transport with low carbon emissions, systematization of existing methods, means and types of neural networks in smart cities, analysis of successful implementation projects. Methods of statistical analysis, linear and non-linear interpolation, logical generalization, comparison, grouping, analysis and synthesis. Results: the analysis of passenger transportation in the city revealed that statistical data sets indicate a decrease in the main indicators of passenger traffic and an increase in the volume of emissions of carbon-containing compounds. The classification of existing approaches to the optimization of public transport is carried out according to the priority of public transport, hybridization and electrification of vehicles and the implementation of IT monitoring. During the systematization of methods and means in smart cities, the following are highlighted: smart transport systems; electric vehicles; transport sharing networks; smart applications and information systems; innovative payment systems; unmanned vehicles; information boards and announcement systems; networks of bicycle paths and equipped sidewalks; environmental monitoring systems. Among neural networks, recurrent, convolutional, and deep neural networks have been proposed as those that contribute to route optimization and traffic prediction. Conclusions: the statistical analysis of passenger transportation established that reducing carbon dioxide emissions is an unresolved task for both public transport and the transportation system. It is proposed to include methods and means that optimize public transport, reducing the carbon footprint of the initiatives of implementing the concept of a smart city, which are successful all over the world. It is proposed to use recurrent, convolutional and deep neural networks to optimize passenger transportation in smart cities.

Designing and developing Intuitionistic Fuzzy Logic Toolbox in MATLAB: Membership and non-membership functions gallery

The authors have designed and developed algorithms for pattern recognition and clustering techniques using intuitionistic fuzzy (IF) sets, IF operators, IF logic (IFL) – shortest path in networks using IF graphs and IF hypergraphs – video processing using temporal IF sets, RGB image representation through IF index matrices, and molecular structure representation through IF directed hypergraphs. The three major steps involved in the above-said modeling processes via IFSs are (i) intuitionistic fuzzification, (ii) modification of membership and non- membership values (using IF logic/operators/rules/relations) and (iii) intuitionistic defuzzification. While developing these algorithms, parameter tuning was one of the major limitations, and hence specific values were assigned to complete the running process. To overcome this, it is necessary to introduce a toolbox in MATLAB so that the users can select the appropriate tools and parameterize them. Hence, in the long process of contributing a full-pledged intuitionistic fuzzy logic toolbox, namely IFL Toolbox in MATLAB, the membership and non-membership functions gallery has been developed initially, as one of the modules which is the foundation for any IFL control system. This module contains functions, codes, examples and figures/graphs, which will be available on the MATLAB creation page. The proposed module is compared with the existing fuzzy logic toolbox in MATLAB and verified.

Stay on trails: Detrimental effects of recreational activities on soil compaction and infiltration

Bromo Tengger Semeru National Park (BTS-NP) in East Java, Indonesia showcases a breathtaking volcanic landscape and cultural allure, drawing hundreds of thousands of domestic and international visitors. Recreational activities involving human and animal trampling and motorized-vehicle traffic caused soil disturbance along their networks of paths, trails, or roads, potentially having a detrimental long-term effect on the tourism industry and environmental services provided by the national parks. However, the extent of the impact of these disturbances on soil properties remains unclear. This study assessed the impact of different disturbance intensities, consisting of undisturbed locations as a control (zone 1), low to medium- (zone 2), and high- (zone 3) disturbance intensities on five different BTS-NP tourism hotspots, including Entrance Wonokitri (EW), Whispering Sand (WS), Parking Temple (PT), Teletubbies Hill (TH), and Entrance Bromo Stairs (EB), on soil properties, including soil compaction represented by soil penetration resistance, and soil infiltration. This study revealed that the higher severity impact of recreational activities on soil compaction was parallel with higher disturbance intensity, particularly in EW and TH. In these particular locations, higher soil compaction is significantly linked to lower soil infiltration, thus needing extra attention and protection. Meanwhile, in WS, PT, and EB, soil infiltration was more controlled by the establishment of a cemented topsoil layer consisting of mixed sand, sulfur, and water. Better management strategies, such as the use of proper trails and road infrastructures, particularly on EW and TH, might be relevant to minimize the impact of recreational activities on these ecologically, economically, and culturally important areas.

From disruption to adaptation: Response of phytoplankton communities in representative impounded lakes to China's South-to-North Water Diversion Project

Impounded lakes are often interconnected in large-scale water diversion projects to form a coordinated system for water allocation and regulation. The alternating runoff and transferred water can significantly impact local ecosystems, which are initially reflected in the sensitive phytoplankton. Nonetheless, limited information is available on the temporal dynamics and assembly patterns of phytoplankton community in impounded lakes responding to continuous and periodic water diversion. Herein, a long-term monitoring from 2013 to 2020 were conducted to systematically investigate the response of phytoplankton community, including its characteristics, stability, and the ecological processes governing community assembly, in representative impounded lakes to the South-to-North Water Diversion Project (SNWDP) in China. In the initial stage of the SNWDP, the phytoplankton diversity indices experienced a decrease during both non-water diversion periods (8.5 %∼21.2 %) and water diversion periods (5.6 %∼12.2 %), implying a disruption in the aquatic ecosystem. But the regular delivery of high-quality water from the Yangtze River gradually increased phytoplankton diversity and mediated ecological assembly processes shifting from stochastic to deterministic. Meanwhile, reduced nutrients restricted the growth of phytoplankton, pushing species to interact more closely to maintain the functionality and stability of the co-occurrence network. The partial least squares path model revealed that ecological process (path coefficient = 0.525, p < 0.01) and interspecies interactions in networks (path coefficient = -0.806, p < 0.01) jointly influenced the keystone and dominant species, ultimately resulting in an improvement in stability (path coefficient = 0.878, p < 0.01). Overall, the phytoplankton communities experienced an evolutionary process from short-term disruption to long-term adaptation, demonstrating resilience and adaptability in response to the challenges posed by the SNWDP. This study revealed the response and adaptation mechanism of phytoplankton communities in impounded lakes to water diversion projects, which is helpful for maintaining the lake ecological health and formulating rational water management strategies.

Analysis of multi-UAV communication networking problem in constrained geographic environments based on improved simulated annealing optimization algorithm

This paper conducts an in-depth exploration of UAV communication networking and path planning within geographically constrained environments. The study begins with necessary data preprocessing, which includes the conversion of latitude and longitude coordinates and the organization of antenna parameters. Through the development of a model based on coordinate geometry, this research effectively identifies communication blind spots and establishes a comprehensive geometric envelope for signal coverage. To enhance network efficiency and path planning, the paper introduces a hybrid optimization algorithm that combines simulated annealing and genetic algorithms. Simulated annealing is used to optimize the selection of communication types between network nodes, while genetic algorithms refine the UAV fleet's flight routes based on these communication strategies. This dual-layered approach allows for fine-tuned adjustments in response to dynamic environmental constraints. The effectiveness of this method is demonstrated through simulations, which reveal a communication coverage rate of 73.7272%. These results confirm that the proposed hybrid optimization technique significantly improves both communication coverage and operational efficiency of UAV networks in complex environments. This provides substantial theoretical insights and practical contributions to the field of UAV network optimization, which is particularly valuable for applications in remote or urban areas where geographical constraints pose significant challenges.

Conditional Information Gain Trellis

Conditional computing processes an input using only part of the neural network’s computational units. Learning to execute parts of a deep convolutional network by routing individual samples has several advantages: This can facilitate the interpretability of the model, reduce the model complexity, and reduce the computational burden during training and inference. Furthermore, if similar classes are routed to the same path, that part of the network learns to discriminate between finer differences and better classification accuracies can be attained with fewer parameters. Recently, several papers have exploited this idea to select a particular child of a node in a tree-shaped network or to skip parts of a network. In this work, we follow a Trellis-based approach for generating specific execution paths in a deep convolutional neural network. We have designed routing mechanisms that use differentiable information gain-based cost functions to determine which subset of features in a convolutional layer will be executed. We call our method Conditional Information Gain Trellis (CIGT). We show that our conditional execution mechanism achieves comparable or better model performance compared to unconditional baselines, using only a fraction of the computational resources. We provide our code and model checkpoints used in the paper at: https://github.com/ufukcbicici/cigt/tree/prl/prl_scripts.

Tetraborylation of p-Benzynes Generated by the Masamune-Bergman Cyclization through Reaction Design Based on the Reaction Path Network.

Designing the reactant molecule of an initial reaction, based on quantum chemical pathway exploration, enabled us to access a new reaction, i.e., the tetraborylation reaction of p-benzynes generated from 1,2-diethynylbenzene derivatives, using bis(pinacolato)diborane(4) (B2pin2). Based on the reaction path network generated via the artificial-force-induced reaction (AFIR) method, desired and undesired paths were identified and used to modify the chemical structure of the reactant. After the in silico screening, the optimal structure of the reactant was determined to be a 1,2-diethynylbenzene derivative with a butylene linker. The reaction of the optimized reactant and its derivatives with an excess of B2pin2 gave the tetraborylated products in good yields (up to 58%). It is quite intriguing that the two carbons of p-benzyne behave formally as dicarbenes in this reaction.