The Climate-system Historical Forecast Project: do stratosphere-resolving models make better seasonal climate predictions in boreal winter?

Using an international, multi-model suite of historical forecasts from the World Climate Research Programme (WCRP) Climate-system Historical Forecast Project (CHFP), we compare the seasonal prediction skill in boreal wintertime between models that resolve the stratosphere and its dynamics (“high-top”) and models that do not (“low-top”). We evaluate hindcasts that are initialized in November, and examine the model biases in the stratosphere and how they relate to boreal wintertime (Dec-Mar) seasonal forecast skill. We are unable to detect more skill in the high-top ensemble-mean than the low-top ensemble-mean in forecasting the wintertime North Atlantic Oscillation, but model performance varies widely. Increasing the ensemble size clearly increases the skill for a given model. We then examine two major processes involving stratosphere-troposphere interactions (the El Niño-Southern Oscillation/ENSO and the Quasi-biennial Oscillation/QBO) and how they relate to predictive skill on intra-seasonal to seasonal timescales, particularly over the North Atlantic and Eurasia regions. High-top models tend to have a more realistic stratospheric response to El Niño and the QBO compared to low-top models. Enhanced conditional wintertime skill over high-latitudes and the North Atlantic region during winters with El Niño conditions suggests a possible role for a stratospheric pathway.

A constrained recursive least squares algorithm for adaptive combination of multiple models

In this paper, we develop a novel constrained recursive least squares algorithm for adaptively combining a set of given multiple models. With data available in an online fashion, the linear combination coefficients of submodels are adapted via the proposed algorithm.We propose to minimize the mean square error with a forgetting factor, and apply the sum to one constraint to the combination parameters. Moreover an l1-norm constraint to the combination parameters is also applied with the aim to achieve sparsity of multiple models so that only a subset of models may be selected into the final model. Then a weighted l2-norm is applied as an approximation to the l1-norm term. As such at each time step, a closed solution of the model combination parameters is available. The contribution of this paper is to derive the proposed constrained recursive least squares algorithm that is computational efficient by exploiting matrix theory. The effectiveness of the approach has been demonstrated using both simulated and real time series examples.

Willingness-to-pay for a probabilistic flood forecast: a risk-based decision-making game

Probabilistic hydro-meteorological forecasts have over the last decades been used more frequently to communicate forecastuncertainty. This uncertainty is twofold, as it constitutes both an added value and a challenge for the forecaster and the user of the forecasts. Many authors have demonstrated the added (economic) value of probabilistic over deterministic forecasts across the water sector (e.g. flood protection, hydroelectric power management and navigation). However, the richness of the information is also a source of challenges for operational uses, due partially to the difficulty to transform the probability of occurrence of an event into a binary decision. This paper presents the results of a risk-based decision-making game on the topic of flood protection mitigation, called “How much are you prepared to pay for a forecast?”. The game was played at several workshops in 2015, which were attended by operational forecasters and academics working in the field of hydrometeorology. The aim of this game was to better understand the role of probabilistic forecasts in decision-making processes and their perceived value by decision-makers. Based on the participants’ willingness-to-pay for a forecast, the results of the game show that the value (or the usefulness) of a forecast depends on several factors, including the way users perceive the quality of their forecasts and link it to the perception of their own performances as decision-makers.

Combination Efficacy of Voriconazole and Amphotericin B in the Experimental Disease in Immunodeficient Mice Caused by Fluconazole-resistant Cryptococcus neoformans

The therapeutic efficacy of amphotericin B and voriconazole alone and in combination with one another were evaluated in immunodeficient mice (BALB/c-SCID) infected with a fluconazole-resistant strain of Cryptococcus neoformans var. grubii. The animals were infected intravenously with 3 x 10(5) cells and intraperitoneally treated with amphotericin B (1.5 mg/kg/day) in combination with voriconazole (40 mg/kg/days). Treatment began 1 day after inoculation and continued for 7 and 15 days post-inoculation. The treatments were evaluated by survival curves and yeast quantification (CFUs) in brain and lung tissues. Treatments for 15 days significantly promoted the survival of the animals compared to the control groups. Our results indicated that amphotericin B was effective in assuring longest-term survival of infected animals, but these animals still harbored the highest CFU of C. neoformans in lungs and brain at the end of the experiment. Voriconazole was not as effective alone, but in combination with amphotericin B, it prolonged survival for the second-longest time period and provided the lowest colonization of target organs by the fungus. None of the treatments were effective in complete eradication of the fungus in mice lungs and brain at the end of the experiment.

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.

A combination of techniques to evaluate photodynamic efficiency of photosensitizers

Photodynamic therapy, used mainly for cancer treatment and microorganisms inaction, is based on production of reactive oxygen species by light irradiation of a sensitizer. Hematoporphyrin derivatives as Photofrin (R) (PF) Photogem (R) (PG) and Photosan (R) (PF), and chlorin-c6-derivatives as Photodithazine (R)(PZ), have suitable sensitizing properties. The present study provides a way to make a fast previous evaluation of photosensitizers efficacy by a combination of techniques: a) use of brovine serum albumin and uric acid as chemical dosimeters; b) photo-hemolysis of red blood cells used as a cell membrane interaction model, and c) octanol/phosphate buffer partition to assess the relative lipophilicity of the compounds. The results suggest the photodynamic efficient rankings PZ > PG >= PF > PS. These results agree with the cytotoxicity of the photosensitizers as well as to chromatographic separation of the HpDs, both performed in our group, showing that the more lipophilic is the dye, the more acute is the damage to the RBC membrane and the oxidation of indol, which is immersed in the hydrophobic region of albumin.

A novel MAP-MRF approach for multispectral image contextual classification using combination of suboptimal iterative algorithms

In this paper we present a novel approach for multispectral image contextual classification by combining iterative combinatorial optimization algorithms. The pixel-wise decision rule is defined using a Bayesian approach to combine two MRF models: a Gaussian Markov Random Field (GMRF) for the observations (likelihood) and a Potts model for the a priori knowledge, to regularize the solution in the presence of noisy data. Hence, the classification problem is stated according to a Maximum a Posteriori (MAP) framework. In order to approximate the MAP solution we apply several combinatorial optimization methods using multiple simultaneous initializations, making the solution less sensitive to the initial conditions and reducing both computational cost and time in comparison to Simulated Annealing, often unfeasible in many real image processing applications. Markov Random Field model parameters are estimated by Maximum Pseudo-Likelihood (MPL) approach, avoiding manual adjustments in the choice of the regularization parameters. Asymptotic evaluations assess the accuracy of the proposed parameter estimation procedure. To test and evaluate the proposed classification method, we adopt metrics for quantitative performance assessment (Cohen`s Kappa coefficient), allowing a robust and accurate statistical analysis. The obtained results clearly show that combining sub-optimal contextual algorithms significantly improves the classification performance, indicating the effectiveness of the proposed methodology. (C) 2010 Elsevier B.V. All rights reserved.

Characterization of O(2) ((1)Delta(g))-Derived Oxidation Products of Tryptophan: A Combination of Tandem Mass Spectrometry Analyses and Isotopic Labeling Studies

The fragmentation mechanisms of singlet oxygen [O(2) ((1)Delta(g))]-derived oxidation products of tryptophan (W) were analyzed using collision-induced dissociation coupled with (18)O-isotopic labeling experiments and accurate mass measurements. The five identified oxidized products, namely two isomeric alcohols (trans and cis WOH), two isomeric hydroperoxides (trans and cis WOOH), and N-formylkynurenine (FMK), were shown to share some common fragment ions and losses of small neutral molecules. Conversely, each oxidation product has its own fragmentation mechanism and intermediates, which were confirmed by (18)O-labeling studies. Isomeric WOH lost mainly H(2)O + CO, while WOOH showed preferential elimination of C(2)H(5)NO(3) by two distinct mechanisms. Differences in the spatial arrangement of the two isomeric WOHs led to differences in the intensities of the fragment ions. The same behavior was also found for trans and cis WOOH. FMK was shown to dissociate by a diverse range of mechanisms, with the loss of ammonia the most favored route. MS/MS analyses, (18)O-labeling, and H(2)(18)O experiments demonstrated the ability of FMK to exchange its oxygen atoms with water. Moreover, this approach also revealed that the carbonyl group has more pronounced oxygen exchange ability compared with the formyl group. The understanding of fragmentation mechanisms involved in O(2) ((1)Delta(g))-mediated oxidation of W provides a useful step toward the structural characterization of oxidized peptides and proteins. (J Am Soc Mass Spectrom 2009, 20, 188-197) (C) 2009 Published by Elsevier Inc. on behalf of American Society for Mass Spectrometry

Ability of Ru complexes for ROMP tuned through a combination of phosphines and amines

Polynorbonerne with high molecular weight was obtained via ring opening metathesis polymerization using catalysts derived from [RuCl(2)(PPh(2)Bz)(2) L] (1 for L = PPh(2) Bz; 2 for L = piperidine) type of complexes when in the presence of ethyl diazoacetate in CHCl(3). The polymer precipitated within a few minutes at 50 degrees C when using 1 with ca. 50% yield ([NBE]/[Ru] = 5000). Regarding 2, for either 30 min at 25 C or 5 min at 50 degrees C, more than 90% of yields are obtained; and at 50 C for 30 min a quantitative yield is obtained. The yield and PDI values are sensitive to the [NBE]/[Ru] ratio. The reaction of 1 with either isonicotinamide or nicotinamide produces six-coordinated complexes of [RuCl(2)(PPh(2)Bz)(2)(L)(2)] type, which are almost inactive and produce only small amounts of polymers at 50 C for 30 min. Thus, we Concluded that the novel complexes show very distinct reactivities for ROMP of NBE. This has been rationalized on account of a combination of synergistic effects of the phosphine-amine ancillary ligands. (C) 2009 Elsevier B.V. All rights reserved.

The value of a saved tree : The lifecycle of CO2-emissions associated with the combination of printing paper and electricity production of wood in the U.S. and Sweden

The pulp- and paper production is a very energy intensive industry sector. Both Sweden and the U.S. are major pulpandpaper producers. This report examines the energy and the CO2-emission connected with the pulp- and paperindustry for the two countries from a lifecycle perspective.New technologies make it possible to increase the electricity production in the integrated pulp- andpaper mill through black liquor gasification and a combined cycle (BLGCC). That way, the mill canproduce excess electricity, which can be sold and replace electricity produced in power plants. In thisprocess the by-products that are formed at the pulp-making process is used as fuel to produce electricity.In pulp- and paper mills today the technology for generating energy from the by-product in aTomlinson boiler is not as efficient as it could be compared to the BLGCC technology. Scenarios havebeen designed to investigate the results from using the BLGCC technique using a life cycle analysis.Two scenarios are being represented by a 1994 mill in the U.S. and a 1994 mill in Sweden.The scenariosare based on the average energy intensity of pulp- and paper mills as operating in 1994 in the U.S.and Sweden respectively. The two other scenarios are constituted by a »reference mill« in the U.S. andSweden using state-of-the-art technology. We investigate the impact of varying recycling rates and totalenergy use and CO2-emissions from the production of printing and writing paper. To economize withthe wood and that way save trees, we can use the trees that are replaced by recycling in a biomassgasification combined cycle (BIGCC) to produce electricity in a power station. This produces extra electricitywith a lower CO2 intensity than electricity generated by, for example, coal-fired power plants.The lifecycle analysis in this thesis also includes the use of waste treatment in the paper lifecycle. Both Sweden and theU.S. are countries that recycle paper. Still there is a lot of paper waste, this paper is a part of the countries municipalsolid waste (MSW). A lot of the MSW is landfilled, but parts of it are incinerated to extract electricity. The thesis hasdesigned special scenarios for the use of MSW in the lifecycle analysis.This report is studying and comparing two different countries and two different efficiencies on theBLGCC in four different scenarios. This gives a wide survey and points to essential parameters to specificallyreflect on, when making assumptions in a lifecycle analysis. The report shows that there arethree key parameters that have to be carefully considered when making a lifecycle analysis of wood inan energy and CO2-emission perspective in the pulp- and paper mill in the U.S. and in Sweden. First,there is the energy efficiency in the pulp- and paper mill, then the efficiency of the BLGCC and last theCO2 intensity of the electricity displaced by BIGCC or BLGCC generatedelectricity. It also show that with the current technology that we havetoday, it is possible to produce CO2 free paper with a waste paper amountup to 30%. The thesis discusses the system boundaries and the assumptions.Further and more detailed research, including amongst others thesystem boundaries and forestry, is recommended for more specificanswers.

Design method for solar heating systems in combination with pellet boilers/stoves

In this study an optimization method for the design of combined solar and pellet heating systems is presented and evaluated. The paper describes the steps of the method by applying it for an example of system. The objective of the optimization was to find the design parameters that give the lowest auxiliary energy (pellet fuel + auxiliary electricity) and carbon monoxide (CO) emissions for a system with a typical load, a single family house in Sweden. Weighting factors have been used for the auxiliary energy use and CO emissions to give a combined target function. Different weighting factors were tested. The results show that extreme weighting factors lead to their own minima. However, it was possible to find factors that ensure low values for both auxiliary energy and CO emissions.

Reducing The Risk Of Floods In Urban Areas With Combined Inland-River System

With the change of the water environment in accordance with climate change, the loss of lives and properties has increased due to urban flood. Although the importance of urban floods has been highlighted quickly, the construction of advancement technology of an urban drainage system combined with inland-river water and its relevant research has not been emphasized in Korea. In addition, without operation in consideration of combined inland-river water, it is difficult to prevent urban flooding effectively. This study, therefore, develops the uncertainty quantification technology of the risk-based water level and the assessment technology of a flood-risk region through a flooding analysis of the combination of inland-river. The study is also conducted to develop forecast technology of change in the water level of an urban region through the construction of very short-term/short-term flood forecast systems. This study is expected to be able to build an urban flood forecast system which makes it possible to support decision making for systematic disaster prevention which can cope actively with climate change.

Real-Time Control Of Floods Along The Demer River, Belgium, By Means Of MPC In Combination With GA And A Fast Conceptual River Model

Climate model projections show that climate change will further increase the risk of flooding in many regions of the world. There is a need for climate adaptation, but building new infrastructure or additional retention basins has its limits, especially in densely populated areas where open spaces are limited. Another solution is the more efficient use of the existing infrastructure. This research investigates a method for real-time flood control by means of existing gated weirs and retention basins. The method was tested for the specific study area of the Demer basin in Belgium but is generally applicable. Today, retention basins along the Demer River are controlled by means of adjustable gated weirs based on fixed logic rules. However, because of the high complexity of the system, only suboptimal results are achieved by these rules. By making use of precipitation forecasts and combined hydrological-hydraulic river models, the state of the river network can be predicted. To fasten the calculation speed, a conceptual river model was used. The conceptual model was combined with a Model Predictive Control (MPC) algorithm and a Genetic Algorithm (GA). The MPC algorithm predicts the state of the river network depending on the positions of the adjustable weirs in the basin. The GA generates these positions in a semi-random way. Cost functions, based on water levels, were introduced to evaluate the efficiency of each generation, based on flood damage minimization. In the final phase of this research the influence of the most important MPC and GA parameters was investigated by means of a sensitivity study. The results show that the MPC-GA algorithm manages to reduce the total flood volume during the historical event of September 1998 by 46% in comparison with the current regulation. Based on the MPC-GA results, some recommendations could be formulated to improve the logic rules.

Modelos de previsão de inflação e estudo da dinâmica inflacionária brasileira

O objetivo dessa dissertação é analisar as variáveis importantes da inflação para a decisão de política econômica do Banco Central. Considerando a importância de reações forward looking das autoridades monetárias num regime de metas de inflação, estudam-se alguns modelos de projeção de inflação de curto prazo para verificar qual modelo possui maior capacidade de previsão. Com o objetivo de entender a dinâmica inflacionária brasileira ao longo desses anos desde a implementação do sistema de metas de inflação, procura-se analisar a dinâmica da inércia inflacionária e do repasse cambial.

Forecasting Brazilian inflation by its aggregate and disaggregated data: a test of predictive power by forecast horizon

This work aims to compare the forecast efficiency of different types of methodologies applied to Brazilian Consumer inflation (IPCA). We will compare forecasting models using disaggregated and aggregated data over twelve months ahead. The disaggregated models were estimated by SARIMA and will have different levels of disaggregation. Aggregated models will be estimated by time series techniques such as SARIMA, state-space structural models and Markov-switching. The forecasting accuracy comparison will be made by the selection model procedure known as Model Confidence Set and by Diebold-Mariano procedure. We were able to find evidence of forecast accuracy gains in models using more disaggregated data