Environmental distribution of per- and polyfluoroalkyl substances (PFAS) on Svalbard: Local sources and long-range transport to the Arctic

The environmental distribution of per- and polyfluoroalkyl substances (PFAS) in water, snow, sediment and soil samples taken along the west coast of Spitsbergen in the Svalbard archipelago, Norwegian Arctic, was determined. The contribution of potential local primary sources (wastewater, firefighting training site at Svalbard airport, landfill) to PFAS concentrations and long-range transport (atmosphere, ocean currents) were then compared, based on measured PFAS levels and composition profiles. In remote coastal and inland areas of Spitsbergen, meltwater had the highest mean ΣPFAS concentration (6.5 ± 1.3 ng L−1), followed by surface snow (2.5 ± 1.7 ng L−1), freshwater (2.3 ± 1.1 ng L−1), seawater (1.05 ± 0.64 ng L−1), lake sediments (0.084 ± 0.038 ng g−1 dry weight (dw)) and marine sediments (−1 dw, median 0.015 ng g−1 dw). Perfluoroalkyl sulfonates (PFSA) and 6:2 fluorotelomer sulfonate (FTSA) were predominant in water and soil samples influenced by local sources, while perfluoroalkyl carboxylates (PFCA) were predominant in water and sediment from remote coastal and inland areas of Svalbard. The PFAS composition profiles observed in remote areas indicated that atmospheric transport and oxidation of volatile precursors is an important source of PFCA on Svalbard. Shorter-chain PFAS such as perfluorobutanoate (PFBA) were the predominant PFAS in freshwater, reflecting replacement of C8-chained PFAS with shorter-chained compounds. The comparatively high PFAS (especially PFBA) concentration in meltwater indicated that melting of snow and ice during the Arctic spring is an important diffuse local PFAS source. This source may become even more important with climate warming-induced melting of Arctic glaciers and ice sheets. Further studies of mobilisation and transport of PFAS in the Arctic region are needed to confirm this trend

Anticipation in Human-Robot Cooperation: A Recurrent Neural Network Approach for Multiple Action Sequences Prediction

Close human-robot cooperation is a key enabler for new developments in advanced manufacturing and assistive applications. Close cooperation require robots that can predict human actions and intent, and understand human non-verbal cues. Recent approaches based on neural networks have led to encouraging results in the human action prediction problem both in continuous and discrete spaces. Our approach extends the research in this direction. Our contributions are three-fold. First, we validate the use of gaze and body pose cues as a means of predicting human action through a feature selection method. Next, we address two shortcomings of existing literature: predicting multiple and variable-length action sequences. This is achieved by introducing an encoder-decoder recurrent neural network topology in the discrete action prediction problem. In addition, we theoretically demonstrate the importance of predicting multiple action sequences as a means of estimating the stochastic reward in a human robot cooperation scenario. Finally, we show the ability to effectively train the prediction model on a action prediction dataset, involving human motion data, and explore the influence of the model's parameters on its performance. Source code repository: https://github.com/pschydlo/ActionAnticipationComment: IEEE International Conference on Robotics and Automation (ICRA) 2018, Accepte

Optimal time sharing in underlay cognitive radio systems with RF energy harvesting

Due to the fundamental tradeoffs, achieving spectrum efficiency and energy efficiency are two contending design challenges for the future wireless networks. However, applying radio-frequency (RF) energy harvesting (EH) in a cognitive radio system could potentially circumvent this tradeoff, resulting in a secondary system with limitless power supply and meaningful achievable information rates. This paper proposes an online solution for the optimal time allocation (time sharing) between the EH phase and the information transmission (IT) phase in an underlay cognitive radio system, which harvests the RF energy originating from the primary system. The proposed online solution maximizes the average achievable rate of the cognitive radio system, subject to the $\varepsilon$-percentile protection criteria for the primary system. The optimal time sharing achieves significant gains compared to equal time allocation between the EH and IT phases.Comment: Proceedings of the 2015 IEEE International Conference on Communications (IEEE ICC 2015), 8-12 June 2015, London, U

Generic Multiuser Coordinated Beamforming for Underlay Spectrum Sharing

The beamforming techniques have been recently studied as possible enablers for underlay spectrum sharing. The existing beamforming techniques have several common limitations: they are usually system model specific, cannot operate with arbitrary number of transmit/receive antennas, and cannot serve arbitrary number of users. Moreover, the beamforming techniques for underlay spectrum sharing do not consider the interference originating from the incumbent primary system. This work extends the common underlay sharing model by incorporating the interference originating from the incumbent system into generic combined beamforming design that can be applied on interference, broadcast or multiple access channels. The paper proposes two novel multiuser beamforming algorithms for user fairness and sum rate maximization, utilizing newly derived convex optimization problems for transmit and receive beamformers calculation in a recursive optimization. Both beamforming algorithms provide efficient operation for the interference, broadcast and multiple access channels, as well as for arbitrary number of antennas and secondary users in the system. Furthermore, the paper proposes a successive transmit/receive optimization approach that reduces the computational complexity of the proposed recursive algorithms. The results show that the proposed complexity reduction significantly improves the convergence rates and can facilitate their operation in scenarios which require agile beamformers computation.Comment: 30 pages, 5 figure

Set Membership Parameter Estimation and Design of Experiments Using Homothety

In this note we address the problems of obtaining guaranteed and as good as possible estimates of system parameters for linear discrete–time systems subject to bounded disturbances. Some existing results relevant for the set–membership parameter identification and outer–bounding are first reviewed. Then, a novel method for characterizing the consistent parameter set based on homothety is offered; the proposed method allows for the utilization of general compact and convex sets for outer–bounding. Based on these results, we consider the one–step input design and identifiability problems in set–membership setting. We provide a guaranteed approach for the one–step input design problem, by selecting optimal inputs for the purpose of parameter estimation. As optimality criterion, the dimension and the outer– bounding volume of the “anticipated ” consistent parameter set is considered. We furthermore derive a sufficient criterion for (one–step) parameter identifiability, i.e. when a point estimate for a parameter can be guaranteed for all possible measurements

Reachability analysis of discrete-time systems with disturbances

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Long-term trends in bottom water chemistry of Swedish lakes

Long-term monitoring of lake water is essential for tracking local and long-range effects of anthro-pogenic pressure and to inform policy aiming at mitigating human environmental impacts. Swedish reference lakes are recovering from acidification; are subject to climate change and increasing inputs of terrestrial dissolved organic matter (DOM). The integrated effects of different environmental pressures alters lake water chemistry and, consequently, affects lake ecosystems. Most time trend analyses have focused on changes in surface water chemistry, whereas less is known about trends in bottom waters. The overall aim of this study was therefore to assess long-term trends (1988-2015) in bottom water chemistry of Swedish reference lakes (n=13) in relation to trends in surface waters. The hypothesis was that the prevalent increasing DOM concentrations in northern lakes have exacerbated the depletion of hypolimnetic dissolved oxygen (DO), as a result of DOM induced prolongation of stratification. Consequently, bottom water NH4-N (nitrate reduction to ammonia) and TP (internal loading) were expected to have increased over time in affected lakes. Time trend analysis (Mann-Kendall, p<0.05) showed that the yearly median bottom water DO has significantly decreased in 7 lakes. In 6 of these lakes (and 2 additional), surface water total organic carbon (TOC) has increased over time, however, rising TOC concentrations were more prevalent in bottom waters (12 lakes). The results further showed that bottom water nitrogen from ammonia (NH4-N) has increased in the 7 lakes with declining DO, whereas bottom water total phosphorus (TP) has increased in 4 of these lakes. In contrast, surface water TP has declined in 7 lakes, which may have masked additional increased internal loading of P. Closer observation of bottom water chemistry revealed that as DO levels dropped, TOC, NH4-N and TP concentrations peaked and were particularly high during periods of sustained anoxia (≥one year). The results also showed that bottom water Si concentrations have increased in most lakes. In the bottom waters of Brunnsjön, a sustained anoxic period coincided with a 32% step change in Si concentration. In conclusion, the underlying mechanism of the observed rising trends in bottom water TOC, NH4-N and TP are likely a result of prolonged or increased incidence of DO depleted hypolimnia. Clearly, monitoring of bottom waters can reveal important aspects of long-term changes in lake water chemistry. Future research is needed to further assess the long-term effects of brownification and climate change on bottom water DO in northern lakes

Approximation of the minimal robustly positively invariant set for discrete-time LTI systems with persistent state disturbances

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Invariant approximations of the minimal robust. positively invariant set

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