Influence of different fibers on the change of pore pressure of self-consolidating concrete exposed to fire

The focus of this paper is given to investigate the effect of different fibers on the pore pressure of fiber reinforced self-consolidating concrete under fire. The investigation on the pore pressure-time and temperature relationships at different depths of fiber reinforced self-consolidating concrete beams was carried out. The results indicated that micro PP fiber is more effective in mitigating the pore pressure than macro PP fiber and steel fiber. The composed use of steel fiber, micro PP fiber and macro PP fiber showed clear positive hybrid effect on the pore pressure reduction near the beam bottom subjected to fire. Compared to the effect of macro PP fiber with high dosages, the effect of micro PP fiber with low fiber contents on the pore pressure reduction is much stronger. The significant factor for reduction of pore pressure depends mainly on the number of PP fibers and not only on the fiber content. An empirical formula was proposed to predict the relative maximum pore pressure of fiber reinforced self-consolidating concrete exposed to fire by considering the moisture content, compressive strength and various fibers. The suggested model corresponds well with the experimental results of other research and tends to prove that the micro PP fiber can be the vital component for reduction in pore pressure, temperature as well spalling of concrete.

Random walk forecast of urban water in Iran under uncertainty

There are two significant reasons for the uncertainties of water demand. On one hand, an evolving technological world is plagued with accelerated change in lifestyles and consumption patterns; and on the other hand, intensifying climate change. Therefore, with an uncertain future, what enables policymakers to define the state of water resources, which are affected by withdrawals and demands? Through a case study based on thirteen years of observation data in the Zayandeh Rud River basin in Isfahan province located in Iran, this paper forecasts a wide range of urban water demand possibilities in order to create a portfolio of plans which could be utilized by different water managers. A comparison and contrast of two existing methods are discussed, demonstrating the Random Walk Methodology, which will be referred to as the â On uncertainty pathâ , because it takes the uncertainties into account and can be recommended to managers. This On Uncertainty Path is composed of both dynamic forecasting method and system simulation. The outcomes show the advantage of such methods particularly for places that climate change will aggravate their water scarcity, such as Iran.

Cultural circuits of climate change in U.K. broadsheet newspapers, 1985–2003

This article argues for a cultural perspective to be brought to bear on studies of climate change risk perception. Developing the “circuit of culture” model, the article maintains that the producers and consumers of media texts are jointly engaged in dynamic, meaning-making activities that are context-specific and that change over time. A critical discourse analysis of climate change based on a database of newspaper reports from three U.K. broadsheet papers over the period 1985–2003 is presented. This empirical study identifies three distinct circuits of climate change—1985–1990, 1991–1996, 1997–2003—which are characterized by different framings of risks associated with climate change. The article concludes that there is evidence of social learning as actors build on their experiences in relation to climate change science and policy making. Two important factors in shaping the U.K.’s broadsheet newspapers’ discourse on “dangerous” climate change emerge as the agency of top political figures and the dominant ideological standpoints in different newspapers.

Assessing the structural change of strategic mobility: determinants under hypercompetitive environments

The primary purpose of this exploratory empirical study is to examine the structural stability of a limited number of alternative explanatory factors of strategic change. On the basis of theoretical arguments and prior empirical evidence from two traditional perspectives, we propose an original empirical framework to analyse whether these potential explanatory factors have remained stable over time in a highly turbulent environment. This original question is explored in a particular setting: the population of Spanish private banks. The firms of this industry have experienced a high level of strategic mobility as a consequence of fundamental changes undergone in their environmental conditions over the last two decades (mainly changes related to the new banking and financial regulation process). Our results consistently support that the effect of most explanatory factors of strategic mobility considered did not remain stable over the whole period of analysis. From this point of view, the study sheds new light on major debates and dilemmas in the field of strategy regarding why firms change their competitive patterns over time and, hence, to what extent the "contextdependency" of alternative views of strategic change as their relative validation can vary over time for a given population. Methodologically, this research makes two major contributions to the study of potential determinants of strategic change. First, the definition and measurement of strategic change employing a new grouping method, the Model-based Cluster Method or MCLUST. Second, in order to asses the possible effect of determinants of strategic mobility we have controlled the non-observable heterogeneity using logistic regression models for panel data.

Forecasting volatility using a continuous time model

This paper evaluates the forecasting performance of a continuous stochastic volatility model with two factors of volatility (SV2F) and compares it to those of GARCH and ARFIMA models. The empirical results show that the volatility forecasting ability of the SV2F model is better than that of the GARCH and ARFIMA models, especially when volatility seems to change pattern. We use ex-post volatility as a proxy of the realized volatility obtained from intraday data and the forecasts from the SV2F are calculated using the reprojection technique proposed by Gallant and Tauchen (1998).

Equity and CO2 emissions distribution in climate change integrated assessment

Emissions distribution is a focus variable for the design of future international agreements to tackle global warming. This paper specifically analyses the future path of emissions distribution and its determinants in different scenarios. Whereas our analysis is driven by tools which are typically applied in the income distribution literature and which have recently been applied to the analysis of CO2 emissions distribution, a new methodological approach is that our study is driven by simulations run with a popular regionalised optimal growth climate change model over the 1995-2105 period. We find that the architecture of environmental policies, the implementation of flexible mechanisms and income concentration are key determinants of emissions distribution over time. In particular we find a robust positive relationship between measures of inequalities.

North-South trade and directed technical change

In a world where poor countries provide weak protection for intellectual property rights (IPRs), market integration shifts technical change in favor of rich nations. Through this channel, free trade may amplify international income differences. At the same time, integration with countries where IPRs are weakly protected can slow down the world growth rate. An important implication of these results is that protection of intellectual property is most beneficial in open countries. This prediction, which is novel in the literature, is consistent with evidence from a panel of 53 countries observed in the years 1965-1990. The paper also provides empirical support for the mechanism linking North-South trade to the direction of technical change: an increase in import penetration from low-wage, low-IPRs, countries is followed by a sharp fall in R&D investment in a panel of US manufacturing sectors.

Renal response to the angiotensin II receptor subtype 1 antagonist irbesartan versus enalapril in hypertensive patients.

OBJECTIVE: To compare the acute and sustained renal hemodynamic effects on hypertensive patients of 100 mg irbesartan and 20 mg enalapril each once daily. PATIENTS: Twenty patients (aged 35-70 years) with uncomplicated, mild-to-moderate essential hypertension and normal serum creatinine levels completed this study. STUDY DESIGN: After random allocation to treatment (n=10 per group), administration schedule (morning or evening) was determined by further random allocation, with crossover of schedules after 6 weeks' therapy. Treatment and administration assignments were double-blind. Twenty-four-hour ambulatory blood pressure was monitored before and after 6 and 12 weeks of therapy. Renal hemodynamics were determined on the first day of drug administration and 12 and 24 h after the last dose during chronic treatment. RESULTS: Administration of each antihypertensive agent induced a renal vasodilatation with no significant change in glomerular filtration rate. However, the time course appeared to differ: irbesartan had no significant acute effect 4 h after the first dose, but during chronic administration a renal vasodilatory response was found 12 and 24 h after the dose; enalapril was effective acutely and 12 h after administration, but no residual effect was found 24 h after the dose. Both antihypertensive agents lowered mean ambulatory blood pressure effectively, with no significant difference between treatments or between administration schedules (morning versus evening). CONCLUSIONS: Irbesartan and enalapril have comparable effects on blood pressure and renal hemodynamics in hypertensive patients with normal renal functioning. However, the time profiles of the renal effects appear to differ, which might be important for long-term renoprotective effects.

Bayesian Inference in the Time Varying Cointegration Model

There are both theoretical and empirical reasons for believing that the parameters of macroeconomic models may vary over time. However, work with time-varying parameter models has largely involved Vector autoregressions (VARs), ignoring cointegration. This is despite the fact that cointegration plays an important role in informing macroeconomists on a range of issues. In this paper we develop time varying parameter models which permit cointegration. Time-varying parameter VARs (TVP-VARs) typically use state space representations to model the evolution of parameters. In this paper, we show that it is not sensible to use straightforward extensions of TVP-VARs when allowing for cointegration. Instead we develop a specification which allows for the cointegrating space to evolve over time in a manner comparable to the random walk variation used with TVP-VARs. The properties of our approach are investigated before developing a method of posterior simulation. We use our methods in an empirical investigation involving a permanent/transitory variance decomposition for inflation.

Sistema d'autenticació one-time password (OTP) per a mòbils

Aquest projecte consisteix en fer l’anàlisi, disseny i implementació d'un sistema d'autenticació a través de contrasenyes d’un sol ús (One Time Password ‐OTP‐) per a dispositius mòbils. Per evitar l’ús de contrasenyes estàtiques farem una aplicació per a telèfons mòbils capaç de generar contrasenyes aleatòries gràcies a uns paràmetres previs, així com de poder tenir un registre dels serveis on poden ser utilitzades. Partirem d’un protocol repte/resposta on l’usuari interactuarà amb el seu telèfon mòbil i un ordinador personal amb una connexió a Internet. Podrà registrar‐se i, introduint certes dades al mòbil que li proporciona el servidor, podrà fer el procés d’autenticar‐se per poder accedir a zones restringides del servei.

Large Time-Varying Parameter VARs

In this paper we develop methods for estimation and forecasting in large timevarying parameter vector autoregressive models (TVP-VARs). To overcome computational constraints with likelihood-based estimation of large systems, we rely on Kalman filter estimation with forgetting factors. We also draw on ideas from the dynamic model averaging literature and extend the TVP-VAR so that its dimension can change over time. A final extension lies in the development of a new method for estimating, in a time-varying manner, the parameter(s) of the shrinkage priors commonly-used with large VARs. These extensions are operationalized through the use of forgetting factor methods and are, thus, computationally simple. An empirical application involving forecasting inflation, real output, and interest rates demonstrates the feasibility and usefulness of our approach.

Dark Clouds or Silver Linings? Knightian Uncertainty and Climate Change

This paper examines the impact of Knightian uncertainty upon optimal climate policy through the prism of a continuous-time real option modelling framework. We analytically determine optimal intertemporal climate policies under ambiguous assessments of climate damages. Additionally, numerical simulations are provided to illustrate the properties of the model. The results indicate that increasing Knightian uncertainty accelerates climate policy, i.e. policy makers become more reluctant to postpone the timing of climate policies into the future.

Time Variation in the Dynamics of Worker Flows: Evidence from the US and Canada

VAR methods have been used to model the inter-relationships between inflows and outfl ows into unemployment and vacancies using tools such as impulse response analysis. In order to investigate whether such impulse responses change over the course of the business cycle or or over time, this paper uses TVP-VARs for US and Canadian data. For the US, we find interesting differences between the most recent recession and earlier recessions and expansions. In particular, we find the immediate effect of a negative shock on both in ow and out flow hazards to be larger in 2008 than in earlier times. Furthermore, the effect of this shock takes longer to decay. For Canada, we fi nd less evidence of time-variation in impulse responses.

Response of photosynthetic organisms to global change: an approach based on marker pigments

Report for the scientific sojourn carried out at the Department of Freshwater Ecology, National Environmetal Research Institute, Denmark, from 2006 to 2008. The main objective of the project was to reconstruct photosynthetic organism community composition using pigmentbased methods and to study their response to natural (e.g. climate) or anthropogenic (e.g. eutrophication) perturbations that took place in the system over time. We performed a study in different locations and at different temporal scales. We analysed the pigment composition in a short sediment record (46 cm sediment depth) of a volcanic lake (Lake Furnas) in the Azores Archipelago (Portugal). The lake has been affected during the last century by successive fish introductions. The specific objective was to reconstruct the lake’s trophic state history and to assess the role of land-use, climate and fish introductions in structuring the lake community. Results obtained suggested that whereas trophic cascade and changes in nutrient concentrations have some clear effects on algal and microbial assemblages, interpreting the effects of changes in climate are not straightforward. This is probably related with the rather constant precipitation in the Azores Islands during the studied period. We also analysed the pigment composition in a long sediment record (1800 cm sediment depth) of Lake Aborre (Denmark) covering ca. 8kyr of lake history. The specific objective was to describe changes in lake primary production and lake trophic state over the Holocene and to determine the photosynthetic organisms involved. Results suggested that external forcing (i.e. land use changes) was responsible of erosion and nutrient run off to the lake that contributed to the reported changes in lake primary production along most of the Holocene.

Model Switching and Model Averaging in Time- Varying Parameter Regression Models

This paper investigates the usefulness of switching Gaussian state space models as a tool for implementing dynamic model selecting (DMS) or averaging (DMA) in time-varying parameter regression models. DMS methods allow for model switching, where a different model can be chosen at each point in time. Thus, they allow for the explanatory variables in the time-varying parameter regression model to change over time. DMA will carry out model averaging in a time-varying manner. We compare our exact approach to DMA/DMS to a popular existing procedure which relies on the use of forgetting factor approximations. In an application, we use DMS to select different predictors in an in ation forecasting application. We also compare different ways of implementing DMA/DMS and investigate whether they lead to similar results.