Underestimated cost of targeted attacks on complex networks

The robustness of complex networks under targeted attacks is deeply connected to the resilience of complex systems, i.e., the ability to make appropriate responses to the attacks. In this article, we investigated the state-of-the-art targeted node attack algorithms and demonstrate that they become very inefficient when the cost of the attack is taken into consideration. In this paper, we made explicit assumption that the cost of removing a node is proportional to the number of adjacent links that are removed, i.e., higher degree nodes have higher cost. Finally, for the case when it is possible to attack links, we propose a simple and efficient edge removal strategy named Hierarchical Power Iterative Normalized cut (HPI-Ncut).The results on real and artificial networks show that the HPI-Ncut algorithm outperforms all the node removal and link removal attack algorithms when the cost of the attack is taken into consideration. In addition, we show that on sparse networks, the complexity of this hierarchical power iteration edge removal algorithm is only $O(n\log^{2+\epsilon}(n))$.Comment: 14 pages, 7 figure

Generalized Network Dismantling

Finding the set of nodes, which removed or (de)activated can stop the spread of (dis)information, contain an epidemic or disrupt the functioning of a corrupt/criminal organization is still one of the key challenges in network science. In this paper, we introduce the generalized network dismantling problem, which aims to find the set of nodes that, when removed from a network, results in a network fragmentation into subcritical network components at minimum cost. For unit costs, our formulation becomes equivalent to the standard network dismantling problem. Our non-unit cost generalization allows for the inclusion of topological cost functions related to node centrality and non-topological features such as the price, protection level or even social value of a node. In order to solve this optimization problem, we propose a method, which is based on the spectral properties of a novel node-weighted Laplacian operator. The proposed method is applicable to large-scale networks with millions of nodes. It outperforms current state-of-the-art methods and opens new directions in understanding the vulnerability and robustness of complex systems.Comment: 6 pages, 5 figure

Unveiling Explosive Vulnerability of Networks through Edge Collective Behavior

Edges, binding together nodes within networks, have the potential to induce dramatic transitions when specific collective failure behaviors emerge. These changes, initially unfolding covertly and then erupting abruptly, pose substantial, unforeseeable threats to networked systems, and are termed explosive vulnerability. Thus, identifying influential edges capable of triggering such drastic transitions, while minimizing cost, is of utmost significance. Here, we address this challenge by introducing edge collective influence (ECI), which builds upon the optimal percolation theory applied to line graphs. ECI embodies features of both optimal and explosive percolation, involving minimized removal costs and explosive dismantling tactic. Furthermore, we introduce two improved versions of ECI, namely IECI and IECIR, tailored for objectives of hidden and fast dismantling, respectively, with their superior performance validated in both synthetic and empirical networks. Finally, we present a dual competitive percolation (DCP) model, whose reverse process replicates the explosive dismantling process and the trajectory of the cost function of ECI, elucidating the microscopic mechanisms enabling ECI's optimization. ECI and the DCP model demonstrate the profound connection between optimal and explosive percolation. This work significantly deepens our comprehension of percolation and provides valuable insights into the explosive vulnerabilities arising from edge collective behaviors.Comment: 19 pages, 11 figures, 2 table

Carbon Dioxide Improves Phosphorus Nutrition by Facilitating the Remobilization of Phosphorus From the Shoot Cell Wall in Rice (Oryza sativa)

Phosphorus (P) starvation leads to increased reutilization of cell wall P in rice (Oryza sativa). Carbon dioxide (CO2) is involved not only in plant growth and development but also in the response to abiotic stresses. However, it remains unclear whether CO2 affects the reutilization of cell wall P in rice when subjected to P deficiency. In the present study, elevated CO2 (600 μl·L−1) significantly increased the soluble P content in shoots when compared with ambient CO2 (400 μl·L−1). This positive effect was accompanied by an increase of pectin content, as well as an increase of pectin methylesterase (PME) activity, which results in P release from the shoot cell wall, making it available for plant growth. P deficiency significantly induced the expression of phosphate transporter genes (OsPT2, OsPT6, and OsPT8) and decreased the P content in the xylem sap, but elevated CO2 had no further effect, indicating that the increased soluble P content observed in shoots under elevated CO2 is attributable to the reutilization of shoot cell wall P. Elevated CO2 further increased the P deficiency-induced ethylene production in the shoots, and the addition of the ethylene precursor 1-amino-cyclopropane-1-carboxylic acid (ACC) mimicked this effect, while the addition of the ethylene inhibitor aminoethoxyvinylglycine (AVG) abolished this effect. These results further support the role of ethylene in the alleviation of P deficiency under elevated CO2. Taken together, our results indicate that the improvement of P nutrition in rice by elevated CO2 is mediated by increasing the shoot cell wall pectin content and PME activity, possibly via the ethylene signaling pathway

EXPERIMENTAL STUDY OF TWO LARGE-SCALE MODELS’ SEAKEEPING PERFORMANCE IN COASTAL WAVES

Actual sea waves and vessel motion are an unsteady nonlinear random process. The currently adopted test to simulate wave impact of vessel models in tank can\u27t fully reveal the impact of real sea waves on vessel swing motion. In this paper the buoy wave height meter is adopted to carry out measurements and analyses of the coastal wave environment. The correlation between the coastal wave spectra and the ocean wave spectra is analyzed. The test system is established for remote control and telemetry self-propelled vessel models suitable for the experiment conducted in the coastal areas. The seakeeping performance test is conducted for the same tonnage of round bilge vessel model and the deep-V hybrid monohull of large-scale vessel model under the coastal wave conditions. The experimental results are compared with the test results of small-scale vessel model in the towing tank. The experimental results show that the seakeeping performance of the deep-V hybrid monohull is improved by a wide margin in contrast to that of the round bilge model, and there is a marked difference between the motion characteristics of large-scale vessel models in the coastal wave environment and that of small-scale vessel models in tank

EVALUATION OF WIND AND WAVE ENVIRONMENT ADAPTABILITY OF SHIPS

The environment adaptability especially integrated sailing performance in rough sea of ships is very important. In this paper the evaluation index system and method of wind and wave environment adaptability were proposed. Then the relative importance of the given indices was analyzed, and the weighting coefficients of the indices were given by estimation matrixes. Besides this the evaluation equation was built. And the AHP method and the method based on fuzzy theory were used for evaluating the environment adaptability of a hybrid monohull and a round bilge monohull. Furthermore, the effect of different models and weighting coefficients given by different matrixes for evaluation results were analyzed. The research indicated that the choice of evaluating parameters had great influence on the evaluation results, and the weighting coefficients were the difficult point but critical point for environment adaptability evaluation of ships