The use of short time-on-stream in situ spectroscopic transient kinetic isotope techniques to investigate the mechanism of hydrocarbon selective catalytic reduction (HC-SCR) of NOx at low temperatures

The problem of differentiating between active and spectator species that have similar infrared spectra has been addressed by developing short time-on-stream in situ spectroscopic transient isotope experimental techniques (STOS-SSITKA). The techniques have been used to investigate the reaction mechanism for the reduction of nitrogen oxides (NOx) by hydrocarbons under lean-burn (excess oxygen) conditions on a silver catalyst. Although a nitrate-type species tracks the formation of isotopically labeled dinitrogen, the results show that this is misleading because a nitrate-type species has the same response to an isotopic switch even under conditions where no dinitrogen is produced. In the case of cyanide and isocyanate species, the results show that it is possible to differentiate between slowly reacting spectator isocyanate species, probably adsorbed on the oxide support, and reactive isocyanate species, possibly on or close to the active silver phase. The reactive isocyanate species responds to an isotope switch at a rate that matches that of the rate of formation of the main product, dinitrogen. It is concluded that these reactive isocyanates could potentially be involved in the reduction of NOx whereas there is no evidence to support the involvement of nitrate-type species that are observable by infrared spectroscopy.

Incremental learning from stream data

Recent years have witnessed an incredibly increasing interest in the topic of incremental learning. Unlike conventional machine learning situations, data flow targeted by incremental learning becomes available continuously over time. Accordingly, it is desirable to be able to abandon the traditional assumption of the availability of representative training data during the training period to develop decision boundaries. Under scenarios of continuous data flow, the challenge is how to transform the vast amount of stream raw data into information and knowledge representation, and accumulate experience over time to support future decision-making process. In this paper, we propose a general adaptive incremental learning framework named ADAIN that is capable of learning from continuous raw data, accumulating experience over time, and using such knowledge to improve future learning and prediction performance. Detailed system level architecture and design strategies are presented in this paper. Simulation results over several real-world data sets are used to validate the effectiveness of this method.

Rift Valley Fever Epidemiology, Surveillance, and Control: What Have Models Contributed?

Background: Rift Valley fever (RVF) is an emerging vector-borne zoonotic disease that represents a threat to human health, animal health, and livestock production, particularly in Africa. The epidemiology of RVF is not well understood, so that forecasting RVF outbreaks and carrying out efficient and timely control measures remains a challenge. Various epidemiological modeling tools have been used to increase knowledge on RVF epidemiology and to inform disease management policies.

Quantifying contributions of domestic wastewater treatment systems to nutrient fluxes in stream headwaters

Effective implementation of the Water Framework Directive requires a reappraisal of conventional approaches to water quality monitoring. Quantifying the impact of domestic wastewater treatment systems (DWWTS) in Irish catchments is further complicated by high levels of natural heterogeneity. This paper presents a numerical model that couples attenuation to flow along different hydrological pathways contributing to river discharge; this permits estimation of the impact of DWWTS to overall nutrient fluxes under a range of geological conditions. Preliminary results suggest high levels of attenuation experienced
before DWWTS effluent reaches bedrock play a significant role in reducing its ecological impact on aquatic receptors. Conversely, low levels of attenuation in systems discharging directly to surface water may affect water quality more significantly, particularly during prolonged dry periods in areas underlain by low productivity aquifers (>60% of Ireland), where dilution capacity is limited.