march networks evidence reviewer password

#March networks evidence reviewer password full

In silicon photonics, he is one of the worldwide recognized experts, he organized several international conferences, workshops and schools and is a frequently invited speaker. His interests encompass also optical sensors or biosensors and solar cells. He is interested in active photonics devices which can be integrated in silicon by using classical waveguides or novel waveguides such as those based on dynamical photonic crystals. During the last years, he concentrated on Silicon based photonics where he looks for the convergence between photonics and electronics by using silicon nanostructures. His research activity concerned the optical properties of semiconductors. He has directed more than 20 PhD students and more than 20 Master thesis students.

He is the president and founder of the IEEE italian chapter on Nanotechnology. He founded the research activity in semiconductor optoelectronics at the University of Trento and started several laboratories of photonics, growth and advanced treatment of materials. He leads the Nanoscience Laboratory (25 people), teaches several classes at the Science Faculty of the University of Trento. 6, 7, 8.Lorenzo Pavesi is Professor of Experimental Physics at the University of Trento (Italy). Outliers are discussed in the text and shown in Fig. γ trunk converges at γ trunk = 2.45 ± 0.27 for network size ≥200 dual-mapped nodes (mean of γ trunk = 2.53 for WDNs and γ trunk = 2.41 for SSNs), except for two WDN outliers (see text) (d) Breaking points between the two power laws of NDD for WDNs (blue circles) and SSNs (red asterisks).

#March networks evidence reviewer password full

We discuss the implications of our findings for the (re-)design of urban infrastructure networks to enhance their resilience to external and internal threats.Ĭharacteristics of node-degree distributions: (a) NDD of SSN in 2005 (3938 dual-mapped nodes, 52,675 primal nodes) follows a (double) PL function with breaking point k break = 10, p ( k ≥ 2 ) = 1.22 k − 2.41 for the trunk, and p ( k > k break ) = 2.74 k − 2.95 for the tail (b) comparison of NDD of similarly sized subnets of WDN (DZ10, circles) and SSN (2005 network, asterisks) highlights the similarity of topologies among SSN and WDN (c) box plot showing heteroscedasticity of PL exponents for the trunks of WDN (hollow) and SSN (dashed) across the full range of subnet sizes (whiskers), outliers (diamonds)].

Deviations from the general topological patterns are indicative of (1) incomplete establishment of network hierarchies and functional network evolution, (2) capacity for growth (expansion) or densification (e.g., in-fill), and (3) likely network vulnerabilities.

These results indicate that generic mechanisms govern the networks’ evolution, similar to those of scale-free networks found in nature. Network graphs for WDNs and SSNs are characterized by several network topological metrics, and a double Pareto (power-law) model approximates the node-degree distributions of both water infrastructure networks (WDN and SSN), across spatial and hierarchical scales relevant to urban settings, and throughout their temporal evolution over several decades.

Graphs were generated using a dual-mapping technique (Hierarchical Intersection Continuity Negotiation), which emphasizes the functional attributes of these networks. Network data were spatially disaggregated into multiple subnets to examine intracity topological differences for functional zones of the WDN and SSN, and time-stamped SSN data were examined to understand network evolution over several decades as the city expanded. We examine high-resolution urban infrastructure data using every pipe for the water distribution network (WDN) and sanitary sewer network (SSN) in a large Asian city (≈4 million residents) to explore the structure as well as the spatial and temporal evolution of these infrastructure networks.