Cascading failures in spatially-embedded random networks

Cascading failures constitute an important vulnerability of interconnected systems. Here we focus on the study of such failures on networks in which the connectivity of nodes is constrained by geographical distance. Specifically, we use random geometric graphs as representative examples of such spatial networks, and study the properties of cascading failures on them in the presence of distributed flow. The key finding of this study is that the process of cascading failures is non-self-averaging on spatial networks, and thus, aggregate inferences made from analyzing an ensemble of such networks lead to incorrect conclusions when applied to a single network, no matter how large the network is. We demonstrate that this lack of self-averaging disappears with the introduction of a small fraction of long-range links into the network. We simulate the well studied preemptive node removal strategy for cascade mitigation and show that it is largely ineffective in the case of spatial networks. We introduce an altruistic strategy designed to limit the loss of network nodes in the event of a cascade triggering failure and show that it performs better than the preemptive strategy. Finally, we consider a real-world spatial network viz. a European power transmission network and validate that our findings from the study of random geometric graphs are also borne out by simulations of cascading failures on the empirical network.Comment: 13 pages, 15 figure

Failure dynamics of the global risk network

Risks threatening modern societies form an intricately interconnected network that often underlies crisis situations. Yet, little is known about how risk materializations in distinct domains influence each other. Here we present an approach in which expert assessments of risks likelihoods and influence underlie a quantitative model of the global risk network dynamics. The modeled risks range from environmental to economic and technological and include difficult to quantify risks, such as geo-political or social. Using the maximum likelihood estimation, we find the optimal model parameters and demonstrate that the model including network effects significantly outperforms the others, uncovering full value of the expert collected data. We analyze the model dynamics and study its resilience and stability. Our findings include such risk properties as contagion potential, persistence, roles in cascades of failures and the identity of risks most detrimental to system stability. The model provides quantitative means for measuring the adverse effects of risk interdependence and the materialization of risks in the network

Gyermekhangképzési hibák és javítási lehetőségeik

Tanulmányom célja bemutatni egyrészt a gyermekek éneklési képességének fejlődési folyamatát a nemzetközi szakirodalmakra támaszkodva, másrészt pedig a longitudinális self-study kutatásom eredményeit ismertetem, amely a gyermekhangképzési hibák és azok javítási lehetőségeinek feltárására, rendszerezésére összpontosult. Kutatásomban a dokumentum-, és tartalomelemzés, a megfigyelés és a self-study módszereket alkalmaztam. A megfigyelési szakaszban 100 általános iskolás gyermek, míg a self-study-ban nyolc 5-6. osztályos diák vett részt. A kutatás során négy fajta gyermekhangképzési hibát különböztettem meg. A hibák okainak, jellemvonásainak feltárása után kidolgozásra kerültek az e hangképzési problémák javítására alkalmas gyakorlattípusok, melyek kipróbálására a longitudinális vizsgálati szakaszban került sor. Eredményességük a gyermekek énekhangképzési hibáinak javítása és az éneklési képesség fejlesztése terén bizonyítottá váltak