Soil nutrient amendment with leaves of varying quality.

Study on the impact of nitrogen and phosphorus fertilization combined with litter amendment on the microbial biomass ofa xanthic Ferralso. (0cm-5cm and 10cm-20cm). First peliminary results after 18 days of incubation showed that soil amended with P tended to have higher microbial C than soils fertilized with N or the control. This result indicates that nitrogen is probably not limiting microbial biomass growth the most , but rather P.bitstream/item/181049/1/ID-2600-2-58.pd

Micorriza arbuscular em cupuaçu e pupunha cultivados em sistema agroflorestal e em monocultivo na Amazônia Central.

O objetivo deste trabalho foi avaliar a colonização micorrízica arbuscular em pupunha (Bactris gasipaes Kunth) e cupuaçu (Theobroma grandiflorum (Willd ex Spring) K. Schum) cultivados em sistema agroflorestal e em monocultivo na Amazônia Central, em duas épocas do ano, e também identificar características anatômicas da formação dessa simbiose nessas espécies

Nitrogen use of a mixed tree crop plantation with a leguminous cover crop.

Study on two associated frut tree crops take up their N in comparison to a leguminous cover crop in an agroforestry system. Preliminar results showed the pueraria had the highest foliar N content followed by pupunha; cupuacu having less than the other two. One year after application, cupuacu took up more of the applied N in comparison to the total N uptake than pueraria and pupunha as seen from the high &15N values. The amount of 15N taken up in relation to dry matter equaled between the three species, since pueraria had a lot higher N contents. The other sampling dates showed the same tendency.bitstream/item/181065/1/ID-2600-2-45-52.pd

Embrapa Trigo: carteira de projetos de pesquisa e desenvolvimento em 2010.

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Embrapa Trigo: carteira de projetos de Pesquisa e Desenvolvimento em 2008.

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Embrapa Trigo: carteira de projetos de pesquisa e desenvolvimento em 2011.

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Assessing the time-frequency co-movements among the five largest engineering consulting companies: A wavelet-base metrics of contagion and VaR ratio

Diversification in a portfolio is an important tool for the systematic risk management that is inherent to different asset classes. The composition of a portfolio with domestic and international assets is seen as one of the main alternatives for building a diversified portfolio, as this approach tends to reduce portfolio return exposure depending on country factors. However, in scenarios where industry factors are predominant, international diversification can increase systematic risk in a portfolio centered on a single asset class. This study is a pioneer in using wavelet-based methods to identify intersectoral co-movements, based on a portfolio of shares of the world’s top five consulting engineering companies, providing an innovative way to be applied to this phenomenon. Our evidence indicates that companies share a strong pattern of co-movements among themselves, especially in cycles of 32 to 64 days, suggesting a higher exposure to risk for portfolios with an investment horizon in long-term cycles.info:eu-repo/semantics/publishedVersio

Importance of Pueraria phaseoloides for the N cycle in tropical tree production.

The objective of this study was to analyse the biomass production and nutrition of pueraria as a cover crop grown in three different agroforestry systems (System 1: cupuacu, pupunha (peach palm), and seringueira (rubber tree); System 2: cupuacu, pupunha and urucu (annatto); System 3: cupuacu, seringueira, coco and citrus) at two levels of fertilisation, and its potential to contribute to the N stocks of the cropping system by biological N2 fixation, in Manaus-AM (Brasil)

Detecting and mitigating adversarial examples in regression tasks: A photovoltaic power generation forecasting case study

With data collected by Internet of Things sensors, deep learning (DL) models can forecast the generation capacity of photovoltaic (PV) power plants. This functionality is especially relevant for PV power operators and users as PV plants exhibit irregular behavior related to environmental conditions. However, DL models are vulnerable to adversarial examples, which may lead to increased predictive error and wrong operational decisions. This work proposes a new scheme to detect adversarial examples and mitigate their impact on DL forecasting models. This approach is based on one-class classifiers and features extracted from the data inputted to the forecasting models. Tests were performed using data collected from a real-world PV power plant along with adversarial samples generated by the Fast Gradient Sign Method under multiple attack patterns and magnitudes. One-class Support Vector Machine and Local Outlier Factor were evaluated as detectors of attacks to Long-Short Term Memory and Temporal Convolutional Network forecasting models. According to the results, the proposed scheme showed a high capability of detecting adversarial samples with an average F1-score close to 90%. Moreover, the detection and mitigation approach strongly reduced the prediction error increase caused by adversarial samples