Intensity Noise Optimization of a Mid-Infrared Frequency Comb Difference Frequency Generation Source

We experimentally demonstrate in a difference-frequency generation mid-infrared frequency comb source the effect of temporal overlap between pump- and signal- pulse to the relative intensity noise (RIN) of the idler pulse. When scanning the temporal delay between our 130 fs long signal- and pump pulses, we observe a RIN minimum with a 3 dB width of 20 fs delay and an RIN increase of 20 dB in 40 fs delay at the edges of this minimum. We also demonstrate active long-term stabilization of the mid-infrared frequency comb source to the temporal overlap setting corresponding to the lowest RIN operation point by an on-line RIN-detector and active feedback control of the pump-signal- pulse delay. This active stabilization set-up allowed us to dramatically increase the signal-to-noise ratio of mid-infrared absorption spectra

Cd and Zn interactions and toxicity in ectomycorrhizal basidiomycetes in axenic culture

Background. Metal contamination in soils affects both above- and belowground communities, including soil microorganisms. Ectomycorrhizal (ECM) fungi are an important component in belowground community and tolerant strains have great potential in enhancing plant-based remediation techniques. We assessed cadmium and zinc toxicity in five ECM species in liquid media (Hebeloma subsaponaceum; H. cylindrosporum; H. crustuliniforme; Scleroderma sp.; Austroboletus occidentalis) and investigated the potential of Zn to alleviate Cd toxicity. Due to highly divergent results reported in the literature, liquid and solid media were compared experimentally for the first time in terms of differential toxicity thresholds in Cd and Zn interactions. Methods. A wide range of Cd and Zn concentrations were applied to ectomycorrhizal fungi in axenic cultures (in mg

Small MAD families whose Isbell-Mr\'owka spaces are pseudocompact

Given a countable transitive model $M$ for ZFC+CH, we prove that one can produce a maximal almost disjoint family in $M$ whose Vietoris Hyperspace of its Isbell-Mr\'owka space is pseudocompact on every Cohen extension of $M$. We also show that a classical example of $\omega_1$-sized maximal almost disjoint family obtained by a forcing iteration of length $\omega_1$ in a model of non CH is such that the Vietoris Hyperspace of its Isbell-Mr\'owka space is pseudocompact.Comment: 14

A Model-Predictive Motion Planner for the IARA Autonomous Car

We present the Model-Predictive Motion Planner (MPMP) of the Intelligent Autonomous Robotic Automobile (IARA). IARA is a fully autonomous car that uses a path planner to compute a path from its current position to the desired destination. Using this path, the current position, a goal in the path and a map, IARA's MPMP is able to compute smooth trajectories from its current position to the goal in less than 50 ms. MPMP computes the poses of these trajectories so that they follow the path closely and, at the same time, are at a safe distance of eventual obstacles. Our experiments have shown that MPMP is able to compute trajectories that precisely follow a path produced by a Human driver (distance of 0.15 m in average) while smoothly driving IARA at speeds of up to 32.4 km/h (9 m/s).Comment: This is a preprint. Accepted by 2017 IEEE International Conference on Robotics and Automation (ICRA

Multiscale streamflow forecasts for the Brazilian hydropower system using bayesian model averaging (BMA)

O uso de sistemas eficientes de previsão de afluências nas diversas escalas temporais permite otimizar a operação do conjunto de reservatórios hidrelétricos brasileiros, elevando o grau de segurança no fornecimento de energia elétrica e minimizando os custos operacionais. Entretanto, os modelos atuais de previsão utilizados pelo Operador Nacional do Sistema Elétrico (ONS) tendem a ser limitados no horizonte de previsão e na modelagem da dependência existente entre as diversas escalas de tempo, reduzindo a qualidade das previsões. Neste trabalho é proposta uma nova contribuição para os modelos de previsão de afluências em uso pelo ONS a partir do conceito de ponderação bayesiana de modelos (BMA), que permite integrar previsões mensais e semanais de vazões com objetivo de melhorar o desempenho das previsões semanais. As previsões mensais são obtidas por meio de um modelo periódico auto-regressivo exógeno (PARX), que busca captar a persistência das vazões na parte auto-regressiva e a contribuição do escoamento superficial na parcela exógena por meio do uso de informações climáticas de larga escala. Previsões semanais de afluência com até seis semanas de antecedência são obtidas a partir das informações disponibilizadas pelo ONS nos relatórios do Programa Mensal de Operação (PMO). A metodologia proposta é aplicada em séries de afluências semanais aos 28 principais reservatórios hidroelétricos brasileiros. Os resultados de previsão semanal de afluências obtidos com a ponderação das saídas dos modelos de previsão semanal e mensal indicam uma melhoria significativa em indicadores de desempenho de previsões (NS, MAPE e DM) quando comparados com os resultados de previsão oriundos do modelo semanal isolado. Os ganhos obtidos nos indicadores de desempenho são mais significativos a partir da segunda semana de antecedência. A abordagem proposta é flexível em termos de implementação, permitindo integrar outras escalas de previsão assim como diferentes modelos preditivos (por exemplo, modelos de base física).The use of efficient streamflow forecast systems at different time scales allows the operational optimization of the Brazilian interconnected hydropower reservoirs, raising the security level of electricity supply and minimizing operating costs. However, current forecasting models used by the National Electric System Operator (ONS) tend to be limited over the forecast horizon and in the modeling of the dependence structure across the various time scales, thus reducing the quality of forecasts. This paper proposes a new contribution to the streamflow forecast models by exploring the concept of Bayesian Model Averaging (BMA), which allows integrating weekly and monthly forecasts in order to improve the skill of weekly predictions. The monthly forecasts are obtained from a periodic auto-regressive exogenous model (PARX), which attempts to capture the persistence of flow in the auto-regressive part and the runoff contribution in the exogenous portion through the use of climate information. Weekly streamflow forecasts with up to six weeks lead time are obtained from information made available by ONS in the Monthly Operational Program (PMO) reports. The proposed methodology is tested using weekly inflow series from the 28 major Brazilian hydropower reservoirs. The weekly streamflow forecasts results obtained from the weighting of the outputs from the weekly and monthly models indicate a significant improvement in skill based on common performance indicators (NS, MAPE and DM) when compared with forecasts derived from the isolated weekly model. The gains in performance indicators are more significant for lead times beyond two weeks. The proposed approach is flexible in terms of implementation, allowing the incorporation of the other forecast scales as well as different forecast models (e.g. physical models)