Spectral representation of EEG data using learned graphs with application to motor imagery decoding

Electroencephalography (EEG) data entail a complex spatiotemporal structure that reflects ongoing organization of brain activity. Characterization of the spatial patterns is an indispensable step in numerous EEG processing pipelines. We present a novel method for transforming EEG data into a spectral representation. First, we learn subject-specific graphs from each subject's EEG data. Second, by eigendecomposition of the normalized Laplacian matrix of each subject's graph, an orthonormal basis is obtained using which any given EEG map of the subject can be decomposed, providing a spectral representation of the data. We show that energy of EEG maps is strongly associated with low frequency components of the learned basis, reflecting the smooth topography of EEG maps. As a proof-of-concept for this alternative view of EEG data, we consider the task of decoding two-class motor imagery (MI) data. To this aim, the spectral representations are first mapped into a discriminative subspace for differentiating two-class data using a projection matrix obtained by the Fukunaga–Koontz transform (FKT). An SVM classifier is then trained and tested on the resulting features to differentiate MI classes. The method is benchmarked against features extracted from a subject-specific functional connectivity matrix as well as four alternative MI-decoding methods on Dataset IVa of BCI Competition III. Experimental results show the superiority of the proposed method over alternative approaches in differentiating MI classes, reflecting the added benefit of (i) decomposing EEG data using data-driven, subject-specific harmonic bases, and (ii) accounting for class-specific temporal variations in spectral profiles

Marine sediments are identified as an environmental reservoir for Escherichia coli : Comparing signature-based and novel amplicon sequencing approaches for microbial source tracking

Viable Escherichia coli were detected in sediments near a point of wastewater discharge in a marine coastal environment in Sweden. Since high concentrations were found in the sediments nearest the pipe, this suggested that treated wastewater effluent was the source of the microbes. In order to examine this hypothesis, different bioinformatics approaches were applied using 16S rRNA gene V3-V4 amplicon sequences from the sediments. Both signature-based source tracking using sequence libraries describing known sources of fecal water pollution (SourceTracker); and, a curated source tracking method, indicated that sediments were contaminated with wastewater. The results from the curated approach were independently confirmed using differential abundance analysis (DESeq2). A number of taxa originating from wastewater were identified which can be used to describe contamination of the sediments, and examine the spread of these specific taxa, even at low relative abundance, along the urban coast. Sequences of phylum Bacteroidetes (such as Bacteroides and Prevotella) and Firmicutes (such as Romboutsia) increased in sediments with higher concentrations of E. coli. In addition, sequences from Trichococcus are proposed as an indicator for treated wastewater. All three source tracking approaches, and the detection of viable E. coli, suggest that urban sediments can be a reservoir for indicator bacteria

The Black Swan problem : The role of capital, liquidity and operating flexibility

How firms cope with tail risk is an under-researched problem in the literature on corporate risk management. This paper presents stylized facts on the nature of revenue shocks based on 65 years worth of Compustat data. We define a Black Swan as an unexpected year-on-year drop in revenue between 30%–90%. The rate of Black Swans has increased markedly since the 1970's and there are more pronounced cyclical peaks in the three most recent decades. We also examine the role of three general determinants of firms’ ability to absorb Black Swans: equity capital, liquidity, and operating flexibility. The conclusion to emerge from this analysis is that the deciding factor in mediating the effects of revenue shocks on employment is liquidity. Cash reserves and cash margins make firms less fragile, but neither equity capital nor operating flexibility robustly buffer against Black Swans. The results continue to hold when we restrict the analysis to transient and cyclical revenue shocks, as well as when we use only a strictly exogenous revenue shock based on the airline industry

Experimental study on effects of ammonia enrichment on the thermoacoustic instability of lean premixed swirling methane flames

Ammonia (NH3) has recently emerged as a promising carbon-free energy carrier. Further development and application of NH3 as fuel in the gas turbine industry can significantly reduce the emissions of carbon dioxide (CO2) and contribute to the achievement of a carbon–neutral society. This study experimentally examined the thermoacoustic instability characteristics of a laboratory-scaled lean premixed gas turbine model combustor operated with different NH3 blending ratios with methane (CH4). Experiments conducted under a wide range of inlet velocities and equivalence ratios suggest that NH3 concentration is critical in determining the characteristics of the instability. Specifically, when the NH3 proportion is less than 50 %, the addition of NH3 causes a mode transition of the instability. However, when the content of NH3 is greater than 50 %, it is shown that the instabilities are suppressed, indicating that the addition of a certain amount of NH3 can enhance the stability of CH4 flames. Additional analysis of flame dynamics reveals that the introduction of NH3 causes the lengthening of the flame front and weakens heat release rate fluctuations in the flame root regions. Further Proper Orthogonal Decomposition (POD) analysis of the flow field shows that the instability modes are strongly coupled with periodic vortex motions of the flow dynamics along the shear layers. Finally, the mode shifting phenomena is successfully predicted by low-order thermoacoustic network modeling. It is suggested that the change in convective time delay caused by NH3 addition is responsible for such transitions

A spatially explicit model of landscape pesticide exposure to bees : Development, exploration, and evaluation

Pesticides represent one of the greatest threats to bees and other beneficial insects in agricultural landscapes. Potential exposure is generated through compound- and crop-specific patterns of pesticide use over space and time and unique degradation behavior among compounds. Realized exposure develops through bees foraging from their nests across the spatiotemporal mosaic of floral resources and associated pesticides throughout the landscape. Despite the recognized importance of a landscape-wide approach to assessing exposure, we lack a sufficiently-evaluated predictive framework to inform mitigation decisions and environmental risk assessment for bees. We address this gap by developing a bee pesticide exposure model that incorporates spatiotemporal pesticide use patterns, estimated rates of pesticide degradation, floral resource dynamics across habitats, and bee foraging movements. We parameterized the model with pesticide use data from a public database containing crop-field- and date-specific records of uses throughout our study region over an entire year. We evaluate the model performance in predicting bee pesticide exposure using a dataset of pesticide residues in pollens gathered by bumble bees (Bombus vosnesenskii) returning to colonies across 14 spatially independent landscapes in Northern California. We applied alternative model formulations of pesticide accumulation and degradation, floral resource seasonality, and bee foraging behavior to evaluate different levels of detail for predicting observed pesticide exposure. Our best model explained 73 % of observed variation in pesticide exposure of bumble bee colonies, with generally positive correlations for the dominant compounds. Timing and location of pesticide use were integral, but more detailed parameterizations of pesticide degradation, floral resources, and bee foraging improved the predictions little if at all. Our results suggest that this approach to predict bees' pesticide exposure has value in extending from the local field scale to the landscape in environmental risk assessment and for exploring mitigation options to support bees in agricultural landscapes

Barriers to better bicycle parking for promoting intermodal journeys : An inter-organisational collaboration perspective

Organisational structures in the transport sector are often complex and fragmented, with different authorities responsible for different stages of a traveller’s journey. In such circumstances, collaboration across organisationalboundaries is required to facilitate intermodal journeys. This paper aims to provide empirically grounded insights into collaboration in transport planning, extending the literature to include cycling as an access and egress mode. This is done by examining the challenge of improving bicycle parking facilities at railway stations in Copenhagen, Denmark. Interviews with key actors involved in public transport and cycling planning reveal three main inter-organisational barriers to improving station bicycle parking in Copenhagen. First, station bicycle parking falls between the responsibilities of different organisations and levels of governance. Second, the absence of an established funding formula contributes to negotiation-oriented rather than collaborative interactions among the stakeholders. Third, the tension between cyclist satisfaction and rail passenger growth targets hinders collective action. In summary, despite Copenhagen’s strong cycling identity and the prioritisation of this transportation mode in the city’s political decision-making and transport planning, the issue of station bicycle parking highlights the complexity of multi-actor governance of intermodal journeys

A conceptual metaheuristic-based framework for improving runoff time series simulation in glacierized catchments

Glacio-hydrological modeling is a key task for assessing the influence of snow and glaciers on water resources, essential for water resources management. The present study aims to enhance a conceptual hydrological model (namely Glacial Snow Melt (GSM)) by data-driven and swarm computing for enhancing the accuracy of rainfall runoff prediction. The proposed framework combines the conceptual hydrological model (i.e. GSM) with the time series predictor model (SVR) and optimization-driven parameter tuning of the firefly algorithm (SVR-FFA). This integration uniquely captures the complex interplay between meteorological variables, glacier processes, and hydrological responses. Applying the hybrid framework proved better results than the standalone GSM and ordinary SVR in simulating runoff time series. The performance of the proposed conceptual integrated metaheuristic-based framework (W-SG-SVR-FFA) demonstrated several enhancements over the standalone GSM model. During the calibration (validation) period, the evaluation metric coefficient of determination (R2) was 0.77 (0.77) for the standalone GSM model and 0.98 (0.91) for the W-SG-SVR-FFA model. The Kling-Gupta Efficiency (KGE) values were 0.81 (0.77) and 0.97 (0.87), respectively. Applying the method in glacierized catchments underscores its importance in areas undergoing swift climate change and glacial melting. This approach enables readers to witness the intricate equilibrium between the model's complexity and the accuracy of simulation outcomes