Network Path Research Articles

Effect of sudden stratosphere warming on characteristics of medium-scale traveling ionospheric disturbances in the Asian region of Russia

Based on the data from the oblique-incidence sounding radio path network at mid- and subauroral latitudes of the Asian region in Russia, we investigated features of medium-scale traveling ionospheric disturbances (MS TID) within the 2016 winter and spring seasons. We revealed characteristic diurnal variations in the occurrence probability and recording time of MS TIDs along different-orientation paths. We analyzed the effect of the 2016 February minor sudden stratosphere warming (SSW) and the 2016 March final warming on the MS TID characteristics in the Asian region of Russia.

Scouting Robot with Search-Space Reducing Hybrid Networks for Unknown Environment Path Planning

Autonomous vehicle navigation is becoming an important problem as fully self-driving cars are becoming a possibility in the next few decades and space exploration is gaining more momentum. One of the most important aspects of autonomous vehicle navigation is a solid path planning algorithm. Most path planning algorithms can be categorized into 2 groups, classical and machine learning algorithms. Classical algorithms can find the shortest path and usually have a 100% accuracy, but it can't function when its environment isn’t 100% mapped out. On the other hand, ML algorithms can operate on partial maps or no maps at all, but they have poor success rate and produce long paths. However, a hybrid approach to a path planning algorithm could eliminate the downside of both categories of algorithms. A robot with a hybrid algorithm could have 100% accuracy while maintaining a near optimum route without a map of the environment. This paper proposes a search space reduction hybrid network (SRHN) path planning algorithm that not only combines the advantages of classical methods and machine learning methods, but also reduces the search space and memory usage. SRHN works by dividing up the distance between a start point and an endpoint with landmarks and paths from its current landmark to the next. While calculating an approximate optimal path, it significantly reduces search space. To test the result of SRHN, experimentation was conducted in the real world. Excellent results have been achieved in the real world 2D tests that were conducted.

Impact of Spatial Inhomogeneity on Excitation Energy Transport in the Fenna-Matthews-Olson Complex.

The dynamics of the excitation energy transfer (EET) in photosynthetic complexes is an interesting question both from the perspective of fundamental understanding and the research in artificial photosynthesis. Over the past decade, very accurate spectral densities have been developed to capture spatial inhomogeneities in the Fenna-Matthews-Olson (FMO) complex. However, challenges persist in numerically simulating these systems, both in terms of parameterizing them and following their dynamics over long periods of time because of long non-Markovian memories. We investigate the dynamics of FMO with the exact treatment of various theoretical spectral densities using the new tensor network path integral-based methods, which are uniquely capable of addressing the difficulty of long memory length and incoherent Förster theory. It is also important to be able to analyze the pathway of EET flow, which can be difficult to identify given the non-trivial structure of connections between bacteriochlorophyll molecules in FMO. We use the recently introduced ideas of relating coherence to population derivatives to analyze the transport process and reveal some new routes of transport. The combination of exact and approximate methods sheds light on the role of coherences in affecting the fine details of the transport and promises to be a powerful toolbox for future exploration of other open systems with quantum transport.

Pushing boundaries: An empirical view on the digital sovereignty of six governments in the midst of geopolitical tensions

In just a few years, the issue of “digital sovereignty” has emerged as an important security issue for governments across the globe, reflecting a growing unease about the security risks associated with government services that depend on foreign service providers for digital infrastructure and traffic routing. This work investigates to which extent government services and communication with citizens relies on infrastructure outside their own jurisdiction for six countries facing sensitive or sometimes even antagonistic relations with neighbors: India, the Netherlands, Pakistan, Taiwan, Ukraine, and the United Kingdom. By combining various methods (traceroute measurements, passive DNS data and geolocation), we determine where and how domains are hosted, as well as the network paths taken by citizens' traffic to them. We uncover different strategies and degrees of autonomy, as well as difficult tradeoffs between different risks to autonomy, some of which might be larger than the risks associated with the dependency on foreign providers. This includes transnational providers being used by all countries, with geopolitical rivals even being tenants on the same network and traffic between citizens and governments regularly traversing international borders. Furthermore, we compared our empirical findings to stated governmental policies and find that they are not always consistent.

Solution algorithms for shortest path network interdiction with symmetric and asymmetric information

We study symmetric and asymmetric models of shortest path network interdiction and propose an algorithm to solve the problem in each case. We consider the situation where the arcs in a network may be interdicted with different levels of intensity, proportional to the budget spent on interdiction. It is assumed in the asymmetric model that the evader’s perception of the interdiction impact is different from that of the interdictor and thus, each player assumes a different objective function. The first algorithm enhances the efficiency of the decomposition based method for the symmetric case by exploiting the problem structure. The second algorithm is devised for solving the asymmetric model and equipped with a customised supplementary procedure to overcome the issue of trapping in a near-optimal solution. Both symmetric and asymmetric models have been solved to optimality using the proposed algorithms. Furthermore, a duality based approach is employed to reformulate both models and solve the resulting problems using a commercial solver. The proposed algorithms significantly outperform the duality based approach for solving large instances and instances with large interdiction budgets.

Biochemical pathways represented by Gene Ontology-Causal Activity Models identify distinct phenotypes resulting from mutations in pathways.

Gene inactivation can affect the process(es) in which that gene acts and causally downstream ones, yielding diverse mutant phenotypes. Identifying the genetic pathways resulting in a given phenotype helps us understand how individual genes interact in a functional network. Computable representations of biological pathways include detailed process descriptions in the Reactome Knowledgebase and causal activity flows between molecular functions in Gene Ontology-Causal Activity Models (GO-CAMs). A computational process has been developed to convert Reactome pathways to GO-CAMs. Laboratory mice are widely used models of normal and pathological human processes. We have converted human Reactome GO-CAMs to orthologous mouse GO-CAMs, as a resource to transfer pathway knowledge between humans and model organisms. These mouse GO-CAMs allowed us to define sets of genes that function in a causally connected way. To demonstrate that individual variant genes from connected pathways result in similar but distinguishable phenotypes, we used the genes in our pathway models to cross-query mouse phenotype annotations in the Mouse Genome Database (MGD). Using GO-CAM representations of 2 related but distinct pathways, gluconeogenesis and glycolysis, we show that individual causal paths in gene networks give rise to discrete phenotypic outcomes resulting from perturbations of glycolytic and gluconeogenic genes. The accurate and detailed descriptions of gene interactions recovered in this analysis of well-studied processes suggest that this strategy can be applied to less well-understood processes in less well-studied model systems to predict phenotypic outcomes of novel gene variants and to identify potential gene targets in altered processes.

A methodological framework to create an urban greenway network promoting avian connectivity: A case study of Curitiba City

Integrating landscape models into urban planning can promote the use of green infrastructure tools in cities, which are essential for meeting future sustainability requirements. The city of Curitiba, the capital of Paraná State, Brazil, provides an opportunity to research methodologies related to habitat connectivity in urban areas. Our study aims to propose a network of greenways that connect the most ecologically relevant green areas. Our methodology integrates avian movement and environmental indicators to enhance urban ecology by leveraging the spatial characteristics of the landscape. In designing the path network, we adapted the parameters to fit the minimum cumulative resistance model, which was based on studies of birds in the urban environment and their dispersion behavior. Our findings indicated that impervious surfaces were the predominant land cover class in the city landscape, while larger forest fragments were primarily situated in the south and northwestern regions. By utilizing least-cost paths, we identified twenty-six priority areas that could be connected to form an ecological network spanning the landscape. Our analysis revealed that the path network was more densely distributed in peripheral areas, where the largest habitats were located, primarily within the city's green belt. Regarding land cover classes along the paths, we found that 39% consisted of forest, 25% of impervious surface, 19% of grass, 16% of bare soil, and 1% of water. Despite being highly urbanized, Curitiba possesses significant potential for restoring avian-related functional connectivity through the urban forest. To aid in the recovery and conservation of ecological corridors along the greenways, we developed a set of guidelines. The information gathered from this study may prove useful for future interventions in the city, and the proposed methodology could serve as a model for greenway planning in other urban areas.

Tracing policy diffusion: Identifying main paths in policy citation networks

Citation-based main path analysis (MPA) has been widely applied to identify developmental trajectories of science and technology, while rarely used to detect paths of policy diffusion. Compared with scientific publications and patents, policy documents show some distinct characteristics, such as citation relationships with different legal validity, which could be considered to improve the policy citation analysis. To this end, this study formally constructs a policy citation network based on a plethora of citing/cited links embedded in the textual content of policy documents and proposes a preference-adjusted main path analysis (PMPA) approach to track historical routes of policy diffusion. PMPA incorporates two kinds of policy citation preferences, including validity bias and time bias. An evidence analysis from China’s new energy policies (NEPs) is implemented to show the efficacy of the proposed approach. The results unveil that the preference-adjusted main path approach can capture more important policies and more informative main paths of policy diffusion than the original MPA. Moreover, our research can yield in-depth insight into the evolutionary process of policy diffusion and provide guidance for policy-makers and industry decision-makers to formulate practical policy-making.

Energy balanced routing protocol based on improved particle swarm optimisation and ant colony algorithm for museum environmental monitoring of cultural relics

AbstractThe environmental monitoring of cultural relics based on wireless sensor networks in museums demands for the transmission and processing of massive data, which in turn leads to problems, such as heavy network traffic, high time delay, and unbalanced node energy consumption. To solve these problems, an energy balanced routing protocol which can minimise the network energy consumption is proposed. The improved swarm intelligence optimisation algorithm which combined particle swarm optimisation with ant colony optimisation is adopted to construct an environmental monitoring system for cultural relics preservation. Simulation results show that the decrease of the path length for data transmission under the proposed algorithm is 2.5% higher than that of other classical algorithms, indicating that the proposed algorithm can effectively reduce the length of network path and improve the transmission performance of network system. In view of optimisation rounds and simulation time, the energy consumption of the network system under our algorithm is 57.5% less compared with other classical algorithms, and the average residual energy variance is 57.1% less. The cultural relics environmental monitoring system based on the proposed routing protocol has better network load balancing performance.

Finding the Optimal Shortest Path in Stochastic Networks Using the Markov Decision Process (MDP)

Finding the Optimal Shortest Path in Stochastic Networks Using the Markov Decision Process (MDP)

Dynamic Neural Network Architecture Design for Predicting Remaining Useful Life of Dynamic Processes

The prediction of remaining useful life is critical in predictive health management. This is done to reduce the expenses associated with operation and maintenance by avoiding errors and failures in dynamic processes. Recently, the abilities of feature classification and automated extraction of neural networks in its convolutional forms have shown fascinating performance when used for estimating the remaining useful life of dynamic processes using deep learning structures. This was accomplished by putting these talents to the task of predicting how long the procedures would be beneficial. Existing network topologies, on the other hand, virtually entirely extract features at a single scale while neglecting important information at other sizes. Meanwhile, because of the architecture of a single network path, the comprehensiveness of the features discovered by these tools is limited. To address these concerns, the authors propose a network structure based on a feature fusion strategy on a parallel multiscale architecture. This structure is then utilized to compute the remaining useful life. This prototype is divided into two sections: the first is a multiscale feature extraction module designed to extract local information features, and the second is a causal convolution module designed to extract global information features by combining multi-layer causal convolution with average pooling. The multiscale feature extraction module is intended for the extraction of local information features, while the causal convolution module is intended for the extraction of global information features. Finally, the two distinct paths are joined to create a fully integrated layer. The simulations and results show that this method has the potential to improve the efficiency and accuracy of estimating the remaining useful life index. Furthermore, the advantages of the established strategy are shown by comparing the results obtained with those produced by applying cutting-edge techniques on a well-known data-set depicting a simulated turbofan engine.

Improving Network Delay Predictions Using GNNs

Autonomous network management is crucial for Fifth Generation (5G) and Beyond 5G (B5G) networks, where a constantly changing environment is expected and network configuration must adapt accordingly. Modeling tools are required to predict the impact on performance (packet and delay loss) when new traffic demands arrives and when changes in routing paths are applied in the network. Mathematical analysis and network simulators are techniques for modeling networks but both have limitations, as the former provides low accuracy and the latter requires high execution times. To overcome these limitations, machine learning (ML) algorithms, and more specifically, graph neural networks (GNNs), are proposed for network modeling due to their ability to capture complex relationships from graph-like data while predicting network properties with high accuracy and low computational requirements. However, one of the main issues when using GNNs is their lack of generalization capability to larger networks, i.e., when trained in small networks (in number of nodes, paths length, links capacity), the accuracy of predictions on larger networks is poor. This paper addresses the GNN generalization problem by the use of fundamental networking concepts. Our solution is built from a baseline GNN model called RouteNet (developed by Barcelona Neural Networking Center-Universitat Politècnica de Catalunya (BNN-UPC)) that predicts the average delay in network paths, and through a number of simple additions significantly improves the prediction accuracy in larger networks. The improvement ratio compared to the baseline model is 101, from a 187.28% to a 1.828%, measured by the Mean Average Percentage Error (MAPE). In addition, we propose a closed-loop control context where the resulting GNN model could be potentially used in different use cases.

Analysis of operator’s activity in control rooms of underground coal mines

The analysis for estimating operator’s activity and quantity of information in control rooms of underground coal mines is presented in this paper. Monitoring of operators in control rooms was conducted in mines Rembas, Soko, Ibar, Jasenovac and Stavalj. The network models are used during the analysis of operator’s activity in control rooms. The result of the analysis is proposed critical path network model. The application of network models improves the quality and level of research, making it more efficient. Most of the analysed information are related to normal and alarm state data received from the mine shaft and operator’s response to this data. Results are given according to actual scope of work depending on the work dynamics in underground coal mines, via histogram of changes in average number of activities per 1 h for 24-h period, then number of operator’s activities per work hour and insight into the global view of technological process in the coal mine with focus on the work scope of mine workers. Analysis of operator’s activities presented in this paper determines the relations between devices in control rooms and operators’ capabilities in order to get better ergonomic design of control rooms.

Efficiently computing alternative paths in game maps

Alternative pathfinding requires finding a set of k alternative paths (including the shortest path) between a given source s and a target t. Intuitively, these paths should be significantly different from each other and meaningful/natural (e.g., must not contain loops or unnecessary detours). While finding alternative paths in road networks has been extensively studied, to the best of our knowledge, we are the first to formally study alternative pathfinding in game maps which are typically represented as Euclidean planes containing polygonal obstacles. First, we adapt the existing techniques designed for road networks to find alternative paths in the game maps. Then, based on our web-based system that visualises alternative paths generated by different approaches, we conduct a user study that shows that the existing road network approaches generate high-quality alternative paths when adapted for the game maps. However, these existing approaches are computationally inefficient especially when compared to the state-of-the-art shortest path algorithms. Motivated by this, we propose novel data structures and exploit these to develop an efficient algorithm to compute high-quality alternative paths. that shows that the existing road network approaches generate high-quality alternative paths in game maps. Our extensive experimental study demonstrates that our proposed algorithm is more than an order of magnitude faster than the existing approaches and returns alternative paths of comparable quality. Furthermore, our algorithm is comparable to a state-of-the-art shortest path algorithm in terms of running time.

Recovering feasibility in real-time conflict-free vehicle routing

Conflict-Free Vehicle Routing Problems (CF-VRPs) arise in manufacturing, transportation and logistics facilities where Automated Guided Vehicles (AGVs) are utilized to move loads. Unlike Vehicle Routing Problems arising in distribution management, CF-VRPs explicitly consider the limited capacity of the arcs of the guide path network to avoid collisions among vehicles. AGV applications have two peculiar features. First, the uncertainty affecting both travel times and machine ready times may result in vehicle delays or anticipations with respect to the fleet nominal plan. Second, the relatively high vehicle speed (in the order of one or two meters per second) requires vehicle plans to be revised in a very short amount of time (usually few milliseconds) in order to avoid collisions. In this paper we present fast exact algorithms to recover plan feasibility in real-time. In particular, we identify two corrective actions that can be implemented in real-time and formulate the problem as a linear program with the aim to optimize four common performance measures (total vehicle delay, total weighted delay, maximum route duration and total lateness). Moreover, we develop tailored algorithms which, tested on randomly generated instances of various sizes, prove to be three orders of magnitude faster than using off-the-shelf solvers.

PEDOT:PSS stabilized paper-based piezoresistive sensor for wearable electronics

As a key component of electronic skins, flexible pressure sensors have attracted more and more attention because of the increasingly growing demand. Stability is a key parameter to evaluate pressure sensors, while relatively few reports have focused on it. Here, a paper-based piezoresistive sensor is developed, in which, the airlaid paper based sensing layer is modified with silver nanowires (AgNWs) and poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) and sandwiched in between two convex electrodes. Due to the cross bonding of PEDOT:PSS membrane, the conductive paths of AgNWs networks are strengthened and stabilized, thus the stability of the sensor is found to be significantly improved. Besides, to regulate the compressibility by varying sensing layers, the performance of the proposed sensor can be further improved, and its practical application performances in healthcare pulse monitoring, tiny muscle motion, and voice recognition are demonstrated. The results confirm that PEDOT:PSS has the potential as stabilization media to AgNWs for paper-based flexible wearable electronics.

From pole-to-ground fault current return paths in a meshed HVDC network to a grounding modelling simplification for protection studies

Meshed bipolar High Voltage Direct Current grids are considered as one of the preferable solutions for integration of renewable energy sources and increasing the security of power systems on a continental scale. In this context, several fault current studies are proposed in the literature, considering different grounding methods for modular multilevel converter neutral points. But these studies often focus on fault current paths to the fault location, and none of them analyzes the return paths of fault current from the fault location. This article deals with fault currents return paths in case of pole-to-ground fault in a grounding configuration using surge arresters in all stations except one, which is solidly grounded. The influence of this solidly grounded point location on the return paths of fault currents is evaluated. With these results, a modelling simplification is considered for HVDC protection studies. Specifically, the discussion investigates whether all MMC neutral points can be solidly grounded.

Research on the evolution path of 5G technology from the perspective of social network analysis: based on the analysis of China and the United States patent citations

ABSTRACT Based on the Derwent patent index database, this paper takes the data source of 5G technology-related patents of China and the United States from January 1, 1998, to December 31, 2019. It uses the social network analysis method to conduct a comparative study on the cooperative innovation network and evolution path of 5G technology between China and the United States. The paper’s results present that: (1) the scale of the 5G technology industry in the two countries continues to expand, and the field of patented technology continues to improve, (2) The number of 5G technology-related patents in China exceeds that in the United States, but China lacks core patents, the quality of 5G technology innovation lags that in the United States, (3) Most of the resources in the innovation networks in China and the US are controlled by large companies, but the level of cooperation among Chinese patentees is not as high as that in the US. (4) Patentees collaborate in innovation networks as frequently as periods change.

Train rescheduling for large-scale disruptions in a large-scale railway network

This paper studies the problem of rescheduling trains in a large-scale railway network with the characteristics of long distance, long time horizon and a large number of trains, where a disruption causes the paralysis of a significant part of the network for a long duration. As rescheduling measures we consider train reordering and retiming as well as the option of rerouting trains along alternative paths in the railway network. Although rerouting was not previously employed in large-scale long-distance networks, we show its benefit in reducing passenger delays. We formulate the problem as an integer linear programming (ILP) model on a space–time network with the goal of minimizing the total passenger delay and the number of passengers that could not reach their destination. In order to effectively solve the ILP model for real-world instances, we propose a heuristic algorithm (LR-H), based on the Lagrangian relaxation (LR) of a subset of constraints in the ILP model. LR allows decomposing the problem into a series of independent train-based subproblems which are easy to solve. Due to the large number of constraints and to cope with the real-time requirement, LR-H employs dynamic constraint-generation.We tested LR-H on railway networks of different sizes under several disruption scenarios: first, we compared the results obtained by LR-H on a small-size instance with those found by the general-purpose ILP solver GUROBI, showing that LR-H can find near-optimal solutions in significantly shorter computing times. Then, we applied LR-H to a real-world instance of a railway network in China with 350 trains in different disruption scenarios. The experimental results show that, LR-H can obtain near-optimal solutions with an average optimality gap of 2.27% in an average computing time of about 300 s.

Using Heuristic Search Techniques to Reduce Task Migrations in Peer-to-Peer Volunteer Computing Networks

Massive computations in today's computer applications necessitate the use of high-performance computing environments. Unfor-tunately, high costs and power management must be addressed while operating these environments. Volunteer computing (VC) enables the creation of a global network of computing devices capable of accumulating their computing power to outperform any supercomputer. VC refers to the use of underutilized computing resources donated by thousands of volunteers who want to actively participate in solving common research problems. However, VC systems experience unexpected and sudden loss of connections between volunteers' computing resources and the main server. In this case, the server must redistribute the work to new devices as they become available. This process is known as task migration, and it is already used in various volunteer frameworks to address the unavailability of computing resources. However, there is a tendency to limit the number of migrations since they are considered a technically complex and time-consuming process. In this paper, we employ heuristic search algorithms to reduce task migrations caused by loss of connections in Peer-to-Peer volunteer networks by locating an alternate network path to send output files to the server when the direct link is no longer available. The simulation results demonstrate that using a heuristic search algorithm eliminates all task migrations caused by loss of connections, resulting in less total execution time and power consumption.