Off-site impacts of agricultural composting: role of terrestrially derived organic matter in structuring aquatic microbial communities and their metabolic potential

While considered as sustainable and low-cost agricultural amendments, the impacts of organic fertilizers on downstream aquatic microbial communities remain poorly documented. We investigated the quantity and quality of the dissolved organic matter leaching from agricultural soil amended with compost, vermicompost or biochar and assessed their effects on lake microbial communities, in terms of viral and bacterial abundances, community structure and metabolic potential. The addition of compost and vermicompost significantly increased the amount of dissolved organic carbon in the leachate compared with soil alone. Leachates from these additions, either with or without biochar, were highly bioavailable to aquatic microbial communities, although reducing the metabolic potential of the community and harbouring more specific communities. Although not affecting bacterial richness or taxonomic distributions, the specific addition of biochar affected the original lake bacterial communities, resulting in a strongly different community. This could be partly explained by viral burst and converging bacterial abundances throughout the samples. These results underline the necessity to include off-site impacts of agricultural amendments when considering their cascading effect on downstream aquatic ecosystems.

A trade-off between maxima in efficiency and specific work output of super- and trans-critical CO2 Brayton cycles

Several operational aspects for thermal power plants in general are non-intuitive and involve simultaneous optimization of a number of operational parameters. In the case of solar operated power plants, it is even more difficult due to varying heat source temperatures induced by variability in insolation levels. This paper introduces a quantitative methodology for load regulation of a CO2 based Brayton cycle power plant using the `thermal efficiency and specific work output' coordinate system. The analysis shows that a transcritical CO2 cycle offers more flexibility under part load performance than the supercritical cycle in case of non-solar power plants. However, for concentrated solar power, where efficiency is important, supercritical CO2 cycle fares better than transcritical CO2 cycle. A number of empirical equations relating heat source temperature, high side pressure with efficiency and specific work output are proposed which could assist in generating control algorithms. (C) 2015 Elsevier B.V. All rights reserved.

Making risk minimization tolerant to label noise

In many applications, the training data, from which one needs to learn a classifier, is corrupted with label noise. Many standard algorithms such as SVM perform poorly in the presence of label noise. In this paper we investigate the robustness of risk minimization to label noise. We prove a sufficient condition on a loss function for the risk minimization under that loss to be tolerant to uniform label noise. We show that the 0-1 loss, sigmoid loss, ramp loss and probit loss satisfy this condition though none of the standard convex loss functions satisfy it. We also prove that, by choosing a sufficiently large value of a parameter in the loss function, the sigmoid loss, ramp loss and probit loss can be made tolerant to nonuniform label noise also if we can assume the classes to be separable under noise-free data distribution. Through extensive empirical studies, we show that risk minimization under the 0-1 loss, the sigmoid loss and the ramp loss has much better robustness to label noise when compared to the SVM algorithm. (C) 2015 Elsevier B.V. All rights reserved.

White light emitting soft materials from off-the-shelf ingredients

Balanced white light emitting systems are important for applications in electronic devices. Of all types of white light emitting materials, gels have the special advantage of easy processability. Here we report two white light emitting gels, which are based on lanthanide cholate self-assembly. The components are commercially available and the gels are prepared by simply sonicating their aqueous solutions (1-3min), unlike any other known white light emitting systems. Their CIE co-ordinates, calculated from the luminescence data, fall in the white light range with a correlated color temperature of ca. 5600 K.

A Simple Label Switching Algorithm for Semisupervised Structural SVMs

In structured output learning, obtaining labeled data for real-world applications is usually costly, while unlabeled examples are available in abundance. Semisupervised structured classification deals with a small number of labeled examples and a large number of unlabeled structured data. In this work, we consider semisupervised structural support vector machines with domain constraints. The optimization problem, which in general is not convex, contains the loss terms associated with the labeled and unlabeled examples, along with the domain constraints. We propose a simple optimization approach that alternates between solving a supervised learning problem and a constraint matching problem. Solving the constraint matching problem is difficult for structured prediction, and we propose an efficient and effective label switching method to solve it. The alternating optimization is carried out within a deterministic annealing framework, which helps in effective constraint matching and avoiding poor local minima, which are not very useful. The algorithm is simple and easy to implement. Further, it is suitable for any structured output learning problem where exact inference is available. Experiments on benchmark sequence labeling data sets and a natural language parsing data set show that the proposed approach, though simple, achieves comparable generalization performance.