Markov and Neural Network Models for Prediction of Structural Deterioration of Stormwater Pipe Assets

Storm-water pipe networks in Australia are designed to convey water from rainfall and surface runoff. They do not transport sewerage. Their structural deterioration is progressive with aging and will eventually cause pipe collapse with consequences of service interruption. Predicting structural condition of pipes provides vital information for asset management to prevent unexpected failures and to extend service life. This study focused on predicting the structural condition of storm-water pipes with two objectives. The first objective is the prediction of structural condition changes of the whole network of storm-water pipes by a Markov model at different times during their service life. This information can be used for planning annual budget and estimating the useful life of pipe assets. The second objective is the prediction of structural condition of any particular pipe by a neural network model. This knowledge is valuable in identifying pipes that are in poor condition for repair actions. A case study with closed circuit television inspection snapshot data was used to demonstrate the applicability of these two models

Corporate Bond Valuation with Both Expected and Unexpected Default

This paper presents three variants of a tractable structural model in which default may take place both expectedly and unexpectedly. The model has the merit of predicting realistically high short term credit spreads. Closed form solutions are provided for corporate bonds (and default swaps) when interest rates are constant or stochastic and when the bond recovery value is exogenous or endogenous to the model. The analysis suggests that, in order for the observed short term yield spreads on high grade corporate bonds to be compensation for credit risk, bond holders must believe that a dramatic sudden plunge in the firm's assets value is possible, even if extremely unlikely.Corporate bond valuation: Structural model; Unexpected default; Short term credit spreads; endogenous bond recovery value; plunge of assets value

How do chemical properties of the atoms change under pressure

Abundant evidence has shown the emergence of dramatic new chemical phenomena under pressure, including the formation of unexpected crystal structures and completely new counterintuitive compounds. In many cases, there is no convincing explanation for these phenomena and there are virtually no chemical rules or models capable of predicting or even rationalizing these phenomena. Here we consider two central chemical properties of atoms, electronegativity and chemical hardness, and determine them as a function of pressure up to 500 GPa. For elements without orbital transfer at high pressure, electronegativity first increases and then decreases, while chemical hardness monotonically decreases as pressure increases. For some active metals, the chemical hardness has a further increase at pressures of the order of tens-hundreds of gigapascals. Furthermore, we discover that orbital transfer, in particular s-d transfer, makes Ni a "pseudo-noble-gas", Fe and Co strong electron acceptors, while Cu and Zn become active metals. We show the explicative and predictive power of our electronegativity and chemical hardness scales under pressure

Fiscal policy and MPC heterogeneity

We use responses to survey questions in the 2010 Italian Survey of Household Income and Wealth that ask consumers how much of an unexpected transitory income change they would consume. We find that the marginal propensity to consume (MPC) is 48 percent on average, and that there is substantial heterogeneity in the distribution. We find that households with low cash-on-hand exhibit a much higher MPC than affluent households, which is in agreement with models with precautionary savings where income risk plays an important role. The results have important implications for the evaluation of fiscal policy, and for predicting household responses to tax reforms and redistributive policies. In particular, we find that a debt-financed increase in transfers of 1 percent of national disposable income targeted to the bottom decile of the cash-on-hand distribution would increase aggregate consumption by 0.82 percent. Furthermore, we find that redistributing 1% of national disposable income from the top to the bottom decile of the income distribution would boost aggregate consumption by 0.33%

Wave height forecasting to improve off-shore access and maintenance scheduling

This paper presents research into modelling and predicting wave heights based on historical data. Wave height is one of the key criteria for allowing access to off-shore wind turbines for maintenance. Better tools for predicting wave height will allow more accurate identification of suitable “weather windows” in which access vessels can be dispatched to site. This in turn improves the ability to schedule maintenance, reducing costs related to vessel dispatch and recall due to unexpected wave patterns. The paper outlines the data available for wave height modelling. Through data mining, different modelling approaches are identified and compared. The advantages and disadvantages of each approach, and their accuracies for a given site implementation, are discussed

Metamaterials: supra\textit{supra}-classical dynamic homogenization

Metamaterials are artificial composite structures designed for controlling waves or fields, and exhibit interaction phenomena that are unexpected on the basis of their chemical constituents. These phenomena are encoded in effective material parameters that can be electronic, magnetic, acoustic, or elastic, and must adequately represent the wave interaction behaviour in the composite within desired frequency ranges. In some cases -- for example, the low frequency regime -- there exist various efficient ways by which effective material parameters for wave propagation in metamaterials may be found. However, the general problem of predicting frequency-dependent dynamic effective constants has remained unsolved. Here, we obtain novel mathematical expressions for the effective parameters of two-dimensional metamaterial systems valid at higher frequencies and wavelengths than previously possible. By way of an example, random configurations of cylindrical scatterers are considered, in various physical contexts: sound waves in a compressible fluid, anti-plane elastic waves, and electromagnetic waves. Our results point towards a paradigm shift in our understanding of these effective properties, and metamaterial designs with functionalities beyond the low-frequency regime are now open for innovation.Comment: 14 pages (including 4 figures and 1 table) in New Journal of Physics, 201

Modeling SMEs Credit Default Risk: The Case of Saudi Arabia

This study assesses the credit risk of small and medium-sized enterprises (SMEs) to minimize unexpected risk events. We construct a hybrid statistical model based on factor analysis and logistic regression to predict enterprise default on loans and determine the factors predicting SMEs default. We assess the credit risk of SMEs listed on the Saudi stock market. The results indicate that the SMEs acid-test ratios are the most influential factors in predicting SMEs credit risk. Therefore, the designed logistic model can be used by financial institutions during the decision-making process of granting loans to SMEs. This study sheds light on challenging access to bank credits due to the lack of financial transparency of most Saudi SMEs