Asset Tracking in Critical Power Communications Infrastructures using Passive Techniques

Active network scanning injects traffic into a network and observes responses to draw conclusions about the network. Passive network analysis works by looking at network meta data or by analyzing traffic as it traverses a fixed point on the network. It may be infeasible or inappropriate to scan critical infrastructure networks. Techniques exist to uniquely map assets without resorting to active scanning. In many cases, it is possible to characterize and identify network nodes by passively analyzing traffic flows. These techniques are considered in particular with respect to their application to power industry critical infrastructure.

An Overview of Public Private Partnership

In many developed and developing countries there has been a move toward an increased reliance on Public Private Partnerships (PPPs) for infrastructure development. This involves an engagement with, or participation of, private companies and the public sector in the financing and provision of infrastructure. In most countries these PPP arrangements have been aimed at overcoming broad public sector constraints in relation to either a lack of public capital; and/or a lack of public sector capacity, resources and specialized expertise to develop, manage, and operate infrastructure assets.
In a number of countries Public Private Partnerships are now commonly used to accelerate economic growth, development and infrastructure delivery and to achieve quality service delivery and good governance. The spectrum of nature and types of public private partnerships (PPPs) are vast, making a precise and complete definition of a PPP difficult. However, significant developments in the use of PPP in many countries have made it increasingly important to understand these practices, as well as to unveil any underlying common principles and problems and to capture and develop a body of good practices, where such can be achieved.

The Size Distribution of US Banks and Credit Unions

This study examines the firm size distribution of US banks and credit unions. A truncated lognormal distribution describes the size distribution, measured using assets data, of a large population of small, community-based commercial banks. The size distribution of a smaller but increasingly dominant cohort of large banks, which operate a high-volume low-cost retail banking model, exhibits power-law behaviour. There is a progressive increase in skewness over time, and Zipf’s Law is rejected as a descriptor of the size distribution in the upper tail. By contrast, the asset size distribution of the population of credit unions conforms closely to the lognormal distribution.

Regulatory Change and Capital Adjustment of US Credit Unions

We investigate the determinants of US credit union capital-to-assets ratios, before and after the implementation of the current capital adequacy regulatory framework in 2000. Capitalization varies pro-cyclically, and until the financial crisis credit unions classified as adequately capitalized or below followed a faster adjustment path than well capitalized credit unions. This pattern was reversed, however, in the aftermath of the crisis. The introduction of the PCA regulatory regime achieved a reduction in the proportion of credit unions classified as adequately capitalized or below that continued until the onset of the crisis. Since the crisis, the speed of recovery of credit unions in this category following an adverse capitalization shock was sharply reduced.

Research-informed design, management and maintenance of infrastructure slopes: development of a multi-scalar approach

The UK’s transport infrastructure is one of the most heavily used in the world. The performance of these networks is critically dependent on the performance of cutting and embankment slopes which make up £20B of the £60B asset value of major highway infrastructure alone. The rail network in particular is also one of the oldest in the world: many of these slopes are suffering high incidents of instability (increasing with time). This paper describes the development of a fundamental understanding of earthwork material and system behaviour, through the systematic integration of research across a range of spatial and temporal scales. Spatially these range from microscopic studies of soil fabric, through elemental materials behaviour to whole slope modelling and monitoring and scaling up to transport networks. Temporally, historical and current weather event sequences are being used to understand and model soil deterioration processes, and climate change scenarios to examine their potential effects on slope performance in futures up to and including the 2080s. The outputs of this research are being mapped onto the different spatial and temporal scales of infrastructure slope asset management to inform the design of new slopes through to changing the way in which investment is made into aging assets. The aim ultimately is to help create a more reliable, cost effective, safer and more resilient transport system.

Time-lapse monitoring of climate effects on earthworks using surface waves

The UK’s transportation network is supported by critical geotechnical assets (cuttings/embankments/dams) that require sustainable, cost-effective management, while maintaining an appropriate service level to meet social, economic, and environmental needs. Recent effects of extreme weather on these geotechnical assets have highlighted their vulnerability to climate variations. We have assessed the potential of surface wave data to portray the climate-related variations in mechanical properties of a clay-filled railway embankment. Seismic data were acquired bimonthly from July 2013 to November 2014 along the crest of a heritage railway embankment in southwest England. For each acquisition, the collected data were first processed to obtain a set of Rayleigh-wave dispersion and attenuation curves, referenced to the same spatial locations. These data were then analyzed to identify a coherent trend in their spatial and temporal variability. The relevance of the observed temporal variations was also verified with respect to the experimental data uncertainties. Finally, the surface wave dispersion data sets were inverted to reconstruct a time-lapse model of S-wave velocity for the embankment structure, using a least-squares laterally constrained inversion scheme. A key point of the inversion process was constituted by the estimation of a suitable initial model and the selection of adequate levels of spatial regularization. The initial model and the strength of spatial smoothing were then kept constant throughout the processing of all available data sets to ensure homogeneity of the procedure and comparability among the obtained VS sections. A continuous and coherent temporal pattern of surface wave data, and consequently of the reconstructed VS models, was identified. This pattern is related to the seasonal distribution of precipitation and soil water content measured on site.