Multi Label Classification of Discrete Data

The paper describes an algorithm for multi-label classification. Since a pattern can belong to more than one class, the task of classifying a test pattern is a challenging one. We propose a new algorithm to carry out multi-label classification which works for discrete data. We have implemented the algorithm and presented the results for different multi-label data sets. The results have been compared with the algorithm multi-label KNN or ML-KNN and found to give good results.

Differentially Selective Chemosensor with Fluorescence Off-On Responses on Cu2+ and Zn2+ Ions in Aqueous Media and Applications in Pyrophosphate Sensing, Live Cell Imaging, and Cytotoxicity

A new benzoyl hydrazone based chemosensor R is synthesized by Schiff base condensation of 2,6-diformyl-4-methylphenol and phenyl carbohydrazide and acts as a highly selective fluorescence sensor for Cu2+ and Zn2+ ions in aqueous media. The reaction of R with CuCl2 or ZnCl2 forms the corresponding dimeric dicopper(II) Cu-2(R)(CH3O)-(NO3)](2)(CH3O)(2) (R-Cu2+) and dizinc(1) Zn-2(R)(2)](NO3)(2) (R-Zn2+) complexes, which are characterized, as R, by conventional techniques including single-crystal X-ray analysis. Electronic absorption and fluorescence titration studies of R with different metal cations in a CH3CN/0.02 M HEPES buffer medium (pH = 7.3) show a highly selective binding affinity only toward Cu(2+)and Zn2+ ions even in the presence of other commonly coexisting ions such as Ne+, K+, Mg2+, Ca2+, Mn2+, Fe2+, Fe3+, Co2+, Ni2+, Cd2+, and Hg2+. Quantification of the fluorescence titration analysis shows that the chemosensor R can indicate the presence of Cu2+ and Zn2+ even at very low concentrations of 17.3 and 16.5 ppb, respectively. R-Zn2+ acts as a selective metal-based fluorescent sensor for inorganic pyrophosphate ion (PPi) even in the presence of other common anions such as F-, Cl-, Br-, I-, CH3COO-, CO32-, HCO3-, N-3(-), SO42-, PPi, AMP, ADP, and ATP in an aqueous medium. The propensity of R as a bioimaging fluorescent probe to detect Cu2+ and Zn2+ ions in human cervical HeLa cancer cell lines and their cytotoxicity against human cervical (HeLa), breast cancer (MCF7), and noncancer breast epithelial (MCF10a) cells have also been investigated. R-Cu2+ shows better cytotoxicity and sensitivity toward cancer cells over noncancer cells than R and R-Zn2+ under identical conditions, with the appearance of apoptotic bodies.

Regulation of Off-Network Pricing in a Nonneutral Network

Representatives of several Internet service providers (ISPs) have expressed their wish to see a substantial change in the pricing policies of the Internet. In particular, they would like to see content providers (CPs) pay for use of the network, given the large amount of resources they use. This would be in clear violation of the ``network neutrality'' principle that had characterized the development of the wireline Internet. Our first goal in this article is to propose and study possible ways of implementing such payments and of regulating their amount. We introduce a model that includes the users' behavior, the utilities of the ISP and of the CPs, and, the monetary flow that involves the content users, the ISP and CP, and, in pUrticular, the CP's revenues from advertisements. We consider various game models and study the resulting equilibria; they are all combinations of a noncooperative game (in which the ISPs and CPs determine how much they will charge the users) with a ``cooperative'' one on how the CP and the ISP share the payments. We include in our model a possible asymmetric weighting parameter (that varies between zero to one). We also study equilibria that arise when one of the CPs colludes with the TSP. We also study two dynamic game models as well as the convergence of prices to the equilibrium values.

Rollover-Preventive Force Synthesis at Active Suspensions in a Vehicle Performing a Severe Maneuver With Wheels Lifted Off

Among the intelligent safety technologies for road vehicles, active suspensions controlled by embedded computing elements for preventing rollover have received a lot of attention. The existing models for synthesizing and allocating forces in such suspensions are conservatively based on the constraints that are valid until no wheels lift off the ground. However, the fault tolerance of the rollover-preventive systems can be enhanced if the smart/active suspensions can intervene in the more severe situation in which the wheels have just lifted off the ground. The difficulty in computing control in the last situation is that the vehicle dynamics then passes into the regime that yields a model involving disjunctive constraints on the dynamics. Simulation of dynamics with disjunctive constraints in this context becomes necessary to estimate, synthesize, and allocate the intended hardware realizable forces in an active suspension. In this paper, we give an algorithm for the previously mentioned problem by solving it as a disjunctive dynamic optimization problem. Based on this, we synthesize and allocate the roll-stabilizing time-dependent active suspension forces in terms of sensor output data. We show that the forces obtained from disjunctive dynamics are comparable with existing force allocations and, hence, are possibly realizable in the existing hardware framework toward enhancing the safety and fault tolerance.

Reduced graphene oxide-titania based platform for label-free biosensor

A label-free biosensor has been fabricated using a reduced graphene oxide (RGO) and anatase titania (ant-TiO2) nanocomposite, electrophoretically deposited onto an indium tin oxide coated glass substrate. The RGO-ant-TiO2 nanocomposite has been functionalized with protein (horseradish peroxidase) conjugated antibodies for the specific recognition and detection of Vibrio cholerae. The presence of Ab-Vc on the RGO-ant-TiO2 nanocomposite has been confirmed using electron microscopy, Fourier transform infrared spectroscopy and electrochemical techniques. Electrochemical studies relating to the fabricated Ab-Vc/RGO-ant-TiO2/ITO immunoelectrode have been conducted to investigate the binding kinetics. This immunosensor exhibits improved biosensing properties in the detection of Vibrio cholerae, with a sensitivity of 18.17 x 10(6) F mol(-1) L-1 m(-2) in the detection range of 0.12-5.4 nmol L-1, and a low detection limit of 0.12 nmol L-1. The association (k(a)), dissociation (k(d)) and equilibrium rate constants have been estimated to be 0.07 nM, 0.002 nM and 0.41 nM, respectively. This Ab-Vc/RGO-ant-TiO2/ITO immunoelectrode could be a suitable platform for the development of compact diagnostic devices.

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.

A Label-Free Multisensing Immunosensor Based on Imaging Ellipsometry

An immunosensor based on imaging ellipsometry and its potential applications was demonstrated in this paper. It has been proven a fast, reliable, and convenient method to quantify the thickness distribution of protein layers or detect protein concentration in solution. Combined with a protein chip, the immunosensor was able to detect multiple analytes simultaneously without any labeling. Preliminary results demonstrated how this immunosensor could be used to monitor several independent biospecific binding processes in real-time and in situ conditions.

Análisis espacial y multitemporal de la cobertura y uso del suelo con base en imágenes de satélites en la subcuenca Río Dipilto, Nueva Segovia, Nicaragua (1993, 2000, 2011)

La cuantificación del cambio de uso del suelo presenta aún altos niveles de incertidumbre, lo que repercute por ejemplo en la estimación de las emisiones de CO 2 . En este estudio se desarrollaron métodos, basados en imágenes de satélite y trabajo de campo, para estimar la tasa de cambio de la cobertura y uso del suelo, y las emisiones de CO 2 en la subcuenca río Dipilto, Nueva Segovia. La superficie de los tipos de vegetación se determinó con imágenes Landsat. Se utilizaron datos de carbono de nueve parcelas de muestreo en bosque de pino que fueron correlacionadas, para establecer un modelo de regresión lineal con el objetivo de estimar el Stock de Carbono. La sobreposición y algebra de mapas se utilizó para el escenario de emisiones de CO 2 . El análisis con imágenes de los años 1993, 2000 y 2011 reveló que durante es tos 18 años la velocidad a la que se perdieron los bosques latifoliados cerrado fue variable. Durante los primeros 7 años (1993 a 2000) se registró un aumento de 99.95 ha , que corresponde a una tasa de deforestación de - 1.45 % anual. Durante los últimos once años (2000 a 2011) esta cantidad cambió totalmente, ya que se eliminaron 331.76 h a , que corresponde a una tasa de deforestación anual de 3.41 %. Finalmente considerando el periodo de análisis, se transformaron más de 232.01 h a por año, correspondiente a u na tasa de deforestación anual de 1.55 %. La imagen de 2011 demostró que las reservas o Stock de C oscila entre 40 - 150 t/ha. Este intervalo de valores fue estimado por un modelo de regresión con razonable ajuste (R2 = 0.73 ), cuyas variables independientes fueron la reflectancia de las distintas bandas como índices de vegetación e infrarrojo cercano. Las pérdidas de C se estimaron en intervalos 1 - 191 t/h a en 20.76% del área. El 32.85% del área se mantuvo estable y 46.39% ganancias de 1 - 210 t/ha. La combinación de imágenes de resolución espacial media como son las de la serie Landsat para definir trayectorias de cambio de la cobertura del suelo, es una opción viable para la solución de interrogantes relacionadas con el cambio climático, tales como la estimación de las emisiones de CO 2 derivadas del cambio de uso del suelo.