The Utility of Land-Surface Model Simulations to Provide Drought Information in a Water Management Context Using Global and Local Forcing Datasets

Drought diagnosis and forecasting are fundamental issues regarding hydrological management in Spain, where recurrent water scarcity periods are normal. Land-surface models (LSMs) could provide relevant information for water managers on how drought conditions evolve. Here, we explore the usefulness of LSMs driven by atmospheric analyses with different resolutions and accuracies in simulating drought and its propagation to precipitation, soil moisture and streamflow through the system. We perform simulations for the 1980-2014 period with SASER (5 km resolution) and LEAFHYDRO (2.5 km resolution), which are forced by the Spanish SAFRAN dataset (at 5km and 30km resolutions), and the global eartH2Observe datasets at 0.25 degrees (including the MSWEP precipitation dataset). We produce standardized indices for precipitation (SPI), soil moisture (SSMI) and streamflow (SSI). The results show that the model structure uncertainty remains an important issue in current generation large-scale hydrological simulations based on LSMs. This is true for both the SSMI and SSI. The differences between the simulated SSMI and SSI are large, and the propagation scales for drought regarding both soil moisture and streamflow are overly dependent on the model structure. Forcing datasets have an impact on the uncertainty of the results but, in general, this impact is not as large as the uncertainty due to model formulation. Concerning the global products, the precipitation product that includes satellite observations (MSWEP) represents a large improvement compared with the product that does not.

     

Ni/Al Intermetallics Plasma Transferred Arc Processing

TAILORING COMPONENT SUFACES for elevatedtemperature operations has been a goal of researchersduring the last century.Process and material selectionare the key factors for this development.In particularlythe latter can be described to have started with thedevelopment of stainless steels.Ni and Co based alloys,the superalloys,attended the increasing technologicaldemands for higher service temperatures(1).Morerecently the higher operating temperatures ofequipments,such as gas turbines,r…     

The Influence of High Temperature Exposure on the Wear Performance of a Ni Based Alloy PTA Coating

Degradation phenomena like wear and corrosion, may have their effects accelerated as components operate at high temperature. The aim of this work is to make an indirect evaluation of the influence of high temperature exposure on wear behavior of Ni coatings. A Ni-Cr-Mo-W alloy, was deposited with Plasma Transferred Arc (PTA) process. An indirect approach based on the Ahrrenius relationship was used to evaluated long time exposures at high temperatures. To simulate longer exposures at lower temperatures, coatings were exposed to higher temperatures for the same time interval. So coatings were soaked in an air furnace at 650℃, 1000℃ and 1200℃. Metal/metal wear behavior was evaluated by pin-on-disc tests. Pins were removed from the coatings by eletrodischarge machining and rubbed against a VC31 quenched and tempered tool steel. Two sets of wear tests were run at 2m/s, for different loads (0.5, 1.0, 2.0 and 3.0Kg) and for different sliding distances (120, 720, 1080, 1440 and 1800m).Coatings characterization was done by scanning electronic microscopy and Vickers microhardness. Results showed as temperature is increased, coatings wear performance is altered,and this is associated with microstructural changes.     

Towards Smooth Particle Methods Without Smoothing

In this article we present a new class of particle methods which aim at being accurate in the uniform norm with a minimal amount of smoothing. The crux of our approach is to compute local polynomial expansions of the characteristic flow to transport the particle shapes with improved accuracy. In the first order case the method consists of representing the transported density with linearly-transformed particles, the second order version transports quadratically-transformed particles, and so on. For practical purposes we provide discrete versions of the resulting LTP and QTP schemes that only involve pointwise evaluations of the forward characteristic flow, and we propose local indicators for the associated transport error. On a theoretical level we extend these particle schemes up to arbitrary polynomial orders and show by a rigorous analysis that for smooth flows the resulting methods converge in \(L^\infty \) without requiring remappings, extended overlapping or vanishing moments for the particles. Numerical tests using different passive transport problems demonstrate the accuracy of the proposed methods compared to basic particle schemes, and they establish their robustness with respect to the remapping period. In particular, it is shown that QTP particles can be transported without remappings on very long periods of time, without hampering the accuracy of the numerical solutions. Finally, a dynamic criterion is proposed to automatically select the time steps where the particles should be remapped. The strategy is a by-product of our error analysis, and it is validated by numerical experiments.     

Probabilistic consensus clustering using evidence accumulation

Machine Learning - Clustering ensemble methods produce a consensus partition of a set of data points by combining the results of a collection of base clustering algorithms. In the evidence...     

Characterizing peers communities and dynamics in a P2P live streaming system

Despite the large number of works devoted to understand P2P live streaming applications, most of them put forth so far rely on characterizing the static view of these systems. In this work, we characterize the SopCast, one of the most important P2P live streaming applications. We focus on its dynamics behavior as well as on the community formation phenomena. Our results show that SopCast presents a low overlay topology diameter and low end-to-end shortest path. In fact, diameter is smaller than 6 hops in almost 90 % of the observation time. More than 96 % of peers’ end-to-end connections present only 3 hops. These values combined may lead to low latencies and a fast streaming diffusion. Second, we show that communities in SopCast are well defined by the streaming data exchange process. Moreover, the SopCast protocol does not group peers according to their Autonomous System. In fact, the probability that a community contains 50 % of its members belonging to the same AS (when we observe the largest AS of our experiments) is lower then 10 %. Peers exchange more data with partners belonging to the same community instead of peers inside the same AS. For the largest AS we have, less than 18 % of peer traffic has been exchanged with another AS partners. Finally, our analysis provides important information to support the future design of more efficient P2P live streaming systems and new protocols that exploit communities’ relationships.     

Assessment of the Impact of Stormwater Characteristics on Clogging in Stormwater Filters

Hydraulic conductivity of granular filter media and its evolution over time is a key design parameter for stormwater filtration and infiltration systems that are now widely used in management of polluted urban runoff. In fact, clogging of filter media is recognised as the main limiting factor of these stormwater treatment systems. This paper focuses on the effect of stormwater characteristics on the clogging of stormwater filters. Effect of five different operational regimes has been tested in this study of sediment concentration; pollutant concentrations; stormwater sediment size; loading rate and stormwater loading/dosing regime and compared with the Base case. For each operational condition, five column replicates were tested. Results suggest that sediment concentration in stormwater is a significant parameter affecting hydraulic and treatment performance, eventually affecting longevity of these stormwater treatment systems. Further, the size of sediments (and their relation to the size of filter media grains) in stormwater was found to be an important parameter to be considered in design of coarse filters with high infiltration rates that are used for stormwater treatment. As expected, the addition of metals and nutrients had limited or no contribution to changes in hydraulic or sediment removal performance of the studied stormwater filters. Whilst loading rate was found to be an important parameter affecting the hydraulic and treatment performance of these systems, any variation in the stormwater loading regime had a limited effect on their performance. This study therefore develops an understanding of the effect of catchment characteristics on design of filters and hence their longevity and maintenance needs.     

Preventing Youth Pregnancy: Dialogue and Deliberation in a Science Museum Exhibit

ABSTRACT

In this article, we explore how visitors engage with a science museum exhibit that displays controversial topics. Through a case study methodology, we focus on the Brazilian exhibit Preventing Youth Pregnancy that delves into teen pregnancy, sexual practices, and sexually transmitted diseases. Using the lens of science communication (an emergent field of research) we explore (a) communication patterns established between the exhibit and the public and (b) the dimensions of engagement that visitors experienced. Data were collected through observations and interviews with visitors, field notes, exit comment cards, and interviews with staffers. Our findings revealed the potential of articulating different models of science communication and exposed a visitor experience in which informed decision making, sharing of personal stories, co-production of knowledge, and learning how to care (and act) were at play while negotiating the complexities of the issues raised by the displays.     

Forecasting binary longitudinal data by a functional PC-ARIMA model

In order to forecast time evolution of a binary response variable from a related continuous time series a functional logit model is proposed. The estimation of this model from discrete time observations of the predictor is solved by using functional principal component analysis and ARIMA modelling of the associated discrete time series of principal components. The proposed model is applied to forecast the risk of drought from El Niño phenomenon.     

A review on the combination of binary classifiers in multiclass problems

Several real problems involve the classification of data into categories or classes. Given a data set containing data whose classes are known, Machine Learning algorithms can be employed for the induction of a classifier able to predict the class of new data from the same domain, performing the desired discrimination. Some learning techniques are originally conceived for the solution of problems with only two classes, also named binary classification problems. However, many problems require the discrimination of examples into more than two categories or classes. This paper presents a survey on the main strategies for the generalization of binary classifiers to problems with more than two classes, known as multiclass classification problems. The focus is on strategies that decompose the original multiclass problem into multiple binary subtasks, whose outputs are combined to obtain the final prediction.