Evolution of social behaviour in the primitively eusocial wasp Ropalidia marginata: do we need to look beyond kin selection?

Ropalidia marginata is a primitively eusocial wasp widely distributed in peninsular India. Although solitary females found a small proportion of nests, the vast majority of new nests are founded by small groups of females. In suchmultiple foundress nests, a single dominant female functions as the queen and lays eggs, while the rest function as sterile workers and care for the queen's brood. Previous attempts to understand the evolution of social behaviour and altruism in this species have employed inclusive fitness theory (kin selection) as a guiding framework. Although inclusive fitness theory is quite successful in explaining the high propensity of the wasps to found nests in groups, several features of their social organization suggest that forces other than kin selection may also have played a significant role in the evolution of this species. These features include lowering of genetic relatedness owing to polyandry and serial polygyny, nest foundation by unrelated individuals, acceptance of young non-nest-mates, a combination of well-developed nest-mate recognition and lack of intra-colony kin recognition, a combination of meek and docile queens and a decentralized self-organized work force, long reproductive queues with cryptic heir designates and conflict-free queen succession, all resulting in extreme intra-colony cooperation and inter-colony conflict.

Selection of Optimal Time-to-go in Generalized Vector Explicit Guidance

A new method of selection of time-to-go (t(go)) for Generalized Vector Explicit Guidance (GENEX) law have been proposed in this paper. t(go) is known to be an important parameter in the control and cost function of GENEX guidance law. In this paper the formulation has been done to find an optimal value of t(go) that minimizes the performance cost. Mechanization of GENEX with this optimal t(go) reduces the lateral acceleration demand and consequently increases the range of the interceptor. This new formulation of computing t(go) comes in closed form and thus it can be implemented onboard. This new formulation is applied in the terminal phase of an surface-to-air interceptor for an angle constrained engagement. Results generated by simulation justify the use of optimal t(go).

Orientation Selection and Microstructural Evolution in Directionally Solidified Tb0.3Dy0.7Fe1.95

Tb0.3Dy0.7Fe1.95 alloy was directionally solidified by using a modified Bridgman technique at a wide range of growth rates of 5 to 100 cm/h. The directionally grown samples exhibited plane front solidification morphology up to a growth rate of 90 cm/h. Typical island banding feature was observed closer to the chilled end, which eventually gave rise to irregular peritectic coupled growth (PCG). The PCG gained prominence with an increase in the growth rate. The texture study revealed formation of strong aOE (c) 311 > texture in a lower growth rate regime, aOE (c) 110 > and ``rotated aOE (c) 110 > aEuroe in an intermediate growth regime, and aOE (c) 112 > in a higher growth rate regime. In-depth analysis of the atomic configuration of a solid-liquid interface revealed that the growth texture is influenced by the kinetics of atomic attachment to the solid-liquid interface, which is intimately related to a planar packing fraction and an atomic stacking sequence of the interfacial plane. The mechanism proposed in this article is novel and will be useful in addressing the orientation selection mechanism of topologically closed packed intermetallic systems. The samples grown at a higher growth rate exhibit larger magnetostriction (lambda) and d lambda/dH owing to the absence of pro-peritectic (Tb,Dy)Fe-3 and formation of aOE (c) 112 > texture, which lies closer to the easy magnetization direction (EMD).

Prototype Selection on Large and Streaming Data

Since streaming data keeps coming continuously as an ordered sequence, massive amounts of data is created. A big challenge in handling data streams is the limitation of time and space. Prototype selection on streaming data requires the prototypes to be updated in an incremental manner as new data comes in. We propose an incremental algorithm for prototype selection. This algorithm can also be used to handle very large datasets. Results have been presented on a number of large datasets and our method is compared to an existing algorithm for streaming data. Our algorithm saves time and the prototypes selected gives good classification accuracy.

Direct Link-Aware Relay Selection for Average Interference-Constrained Underlay Cognitive Radio

Cooperative relaying combined with selection exploits spatial diversity to significantly improve the performance of interference-constrained secondary users in an underlay cognitive radio network. We present a novel and optimal relay selection (RS) rule that minimizes the symbol error probability (SEP) of an average interference-constrained underlay secondary system that uses amplify-and-forward relays. A key point that the rule highlights for the first time is that, for the average interference constraint, the signal-to-interference-plus-noise-ratio (SINR) of the direct source-to-destination (SI)) link affects the choice of the optimal relay. Furthermore, as the SINR increases, the odds that no relay transmits increase. We also propose a simpler, more practical, and near-optimal variant of the optimal rule that requires just one bit of feedback about the state of the SD link to the relays. Compared to the SD-unaware ad hoc RS rules proposed in the literature, the proposed rules markedly reduce the SEP by up to two orders of magnitude.

Simulating dynamic covariance structures for testing the adaptive behavior of variable selection algorithms (Invited Paper)

Variable selection for regression is a classical statistical problem, motivated by concerns that too large a number of covariates may bring about overfitting and unnecessarily high measurement costs. Novel difficulties arise in streaming contexts, where the correlation structure of the process may be drifting, in which case it must be constantly tracked so that selections may be revised accordingly. A particularly interesting phenomenon is that non-selected covariates become missing variables, inducing bias on subsequent decisions. This raises an intricate exploration-exploitation tradeoff, whose dependence on the covariance tracking algorithm and the choice of variable selection scheme is too complex to be dealt with analytically. We hence capitalise on the strength of simulations to explore this problem, taking the opportunity to tackle the difficult task of simulating dynamic correlation structures. © 2008 IEEE.

Streaming covariance selection with applications to adaptive querying in sensor networks

Sensor networks can be naturally represented as graphical models, where the edge set encodes the presence of sparsity in the correlation structure between sensors. Such graphical representations can be valuable for information mining purposes as well as for optimizing bandwidth and battery usage with minimal loss of estimation accuracy. We use a computationally efficient technique for estimating sparse graphical models which fits a sparse linear regression locally at each node of the graph via the Lasso estimator. Using a recently suggested online, temporally adaptive implementation of the Lasso, we propose an algorithm for streaming graphical model selection over sensor networks. With battery consumption minimization applications in mind, we use this algorithm as the basis of an adaptive querying scheme. We discuss implementation issues in the context of environmental monitoring using sensor networks, where the objective is short-term forecasting of local wind direction. The algorithm is tested against real UK weather data and conclusions are drawn about certain tradeoffs inherent in decentralized sensor networks data analysis. © 2010 The Author. Published by Oxford University Press on behalf of The British Computer Society. All rights reserved.

Bayesian model selection for time series using Markov chain Monte Carlo

We present a stochastic simulation technique for subset selection in time series models, based on the use of indicator variables with the Gibbs sampler within a hierarchical Bayesian framework. As an example, the method is applied to the selection of subset linear AR models, in which only significant lags are included. Joint sampling of the indicators and parameters is found to speed convergence. We discuss the possibility of model mixing where the model is not well determined by the data, and the extension of the approach to include non-linear model terms.

Estudio del proceso de implementación y certificación del sistema HACCP en las Empresas Callejas, S.A., Exotic Food, Apronot y Tropifrutas

El presente trabajo fue realizado en las empresas procesadoras de frutas tropicales de Nicaragua que han venido desarrollando actividades encaminadas a la certificación HACCP según datos suministrados por el Ministerio Agropecuario y Forestal (MAG-FOR), las empresas visitadas fueron: APRONOT, ubicada en el Municipio de San Marcos Departamento de Carazo, La empresa Callejas Sequeira, S.A. ubicada en la ciudad de Granada, La empresa Exotic Food ubicada en el Km 107 carretera a Rivas y la empresa TROPIFRUTAS ubicada en Nueva Guinea, Región Autónoma del Atlántico Sur. Se consultaron fuentes secundarías existentes en el país sobre el Análisis de Riesgos y Críticos Puntos de Control (HACCP), así como información de Internet. Para conocer la situación actual del proceso de certificación y adopción del Análisis de Riesgos y Críticos Puntos de Control (HACCP) en la Industria Agroalimentaria se procedió a entrevistar a funcionarios del Ministerio Agropecuario y Forestal (MAGFOR), y del Ministerio de Fomento, Industria y Comercio (MIFIC). La industria agroalimentaria de Nicaragua ha adoptado el sistema de Análisis de Riesgos y Críticos Puntos de Control (HACCP) y se encuentra en un proceso de verificación y Auditoria para obtener la certificación, todas las empresas entrevistadas mostraron toda la voluntad de certificar su producto ya que les abre las puertas en la búsqueda de mejores oportunidades de mercados para sus productos. Las mayores dificultades para lograr la certificación radican en la falta de recursos financieros para ejecutar las recomendaciones de los inspectores, así como la dificultad de garantizar la trazabilidad del origen de la materia prima.