Effects of wall thermal conductivity on entropy generation and exergy losses in a H2-air premixed flame microcombustor

Microcombustion is a promising method for fulfilling the energy requirements of small-scale systems currently powered by portable batteries. However, its applications rely upon mitigation of heat losses, which adversely affect flame stability and performance. Heat losses in turn depend upon wall properties, especially thermal conductivity. It is thus necessary to systematically investigate the relationship between wall thermal conductivity and microcombustor performance using the exergy analysis. In this work, entropy generation rates of different irreversible processes in an annular microcombustor were computed for stoichiometric hydrogen-air mixture using CFD simulations of reactive flow for wall thermal conductivities in the range 0.1-325 W/m K. Chemical reaction, heat conduction, and mass diffusion were the dominant contributors to entropy generation, in the decreasing order. Irreversibilities due to combustion decreased as thermal conductivities increased. Diffusion contributions were most sensitive to the changes in thermal conductivity but chemical reaction and heat conduction contributions changed marginally. Results showed that walls did not contribute significantly to entropy generation, but increased wall heat losses at higher thermal conductivities adversely affected the exergetic performance of microcombustor through availability losses and by influencing the flow gradients. Based on the results of this study, wall thermal conductivity in the range 0.1-1.75 W/m K was found suitable in order to obtain uniform wall temperature profiles and high exergetic efficiencies.     

A sustainable and collaborative strategy for dynamic spectrum management in next generation wireless networks

Next generation wireless technologies offer various services from voice call to full motion pictures and even to high speed internet access. Consequently, the service providers (SP) armed with different wireless technologies (like 2.5G/3G/LTE) would require an adequate and significant amount of spectrum bandwidth for satisfying the need of their customers. Hence to achieve complete commercialization, the SPs, operating simultaneously, would demand for more and more spectrum from the regulatory body of the country. The spectrum demand on the part of the SP may vary with time (dynamic) because of varied kind of loads which are generated depending on the nature of the client-base, their requirements and their expected quality of experience. This work has addressed this challenging issue of allocating spectrum dynamically to different technologies under the portfolio of an SP. Here, we have conceived a scenario where service providers (SP) own multiple access networks (ANs) of different technologies. We envisage that an entity, called local spectrum controller (LSC) which is dedicated for managing the common pool of spectrum allocated to each SP. LSC is mainly responsible for distributing the spectrum to individual ANs of an SP in a fair manner. Since the available spectrum may not be sufficient enough to satisfy the aggregate demand from all ANs simultaneously, an LSC may face a situation, where satisfying individual demands from all ANs may result in a compromise between the demand and supply. This demand–supply situation would force an LSC or an SP to adhere to some dynamic spectrum management strategy, where demands of an AN would have to be satisfied depending on the current state of available spectrum and required usage of it. This calls for an adaptive dynamic strategy to be introduced by an SP for efficient spectrum distribution. The dynamic disparity of spectrum allocation can be idealized as a game between LSC and ANs. Hence, in the present work, we have modeled the problem of dynamic spectrum allocation as an n-player cooperative bankruptcy game and have solved the problem with the help of Shapley value and τ-value separately. We have investigated whether the ANs find it beneficial to cooperate with each other to make the solution sustainable enough. To evaluate the performances of the games that the ANs play, we have designed a novel utility function for each AN. We have identified plausible aims of an SP as minimizing overall dissatisfaction (MOD) and maximizing equality of distribution (MED). Next, we have studied performances of the above two solution concepts against max–min fairness algorithm (benchmarked in our case) with respect to the above objectives of LSC. Finally, we have proposed a unique heuristic in order to facilitate the decision making process of dynamic spectrum allocation, which leads to an adaptive yet optimized spectrum allocation strategy.     

The classification game: combining supervised learning and language evolution

We study the emergence of shared representations in a population of agents engaged in a supervised classification task, using a model called the classification game. We connect languages with tasks by treating the agents’ classification hypothesis space as an information channel. We show that by learning through the classification game, agents can implicitly perform complexity regularisation, which improves generalisation. Improved generalisation also means that the languages that emerge are well adapted to the given task. The improved language-task fit springs from the interplay of two opposing forces: the dynamics of collective learning impose a preference for simple representations, while the intricacy of the classification task imposes a pressure towards representations that are more complex. The push–pull of these two forces results in the emergence of a shared representation that is simple but not too simple. Our agents use artificial neural networks to solve the classification tasks they face, and a simple counting algorithm to learn a language as a form-meaning mapping. We present several experiments to demonstrate that both compositional and holistic languages can emerge in our system. We also demonstrate that the agents avoid overfitting on noisy data, and can learn some very difficult tasks through interaction, which they are unable to learn individually. Further, when the agents use simple recurrent networks to solve temporal classification tasks, we see the emergence of a rudimentary grammar, which does not have to be explicitly learned.     

A dynamic spectrum management strategy based on cooperative bargaining framework

This work proposes a dynamic and fair spectrum management strategy to be adopted by a service provider (SP). Here, we consider a scenario where multiple access networks (ANs) of different (not necessarily competing) technologies are owned by a single SP. We envisage that an SP employs an entity, called local spectrum controller (LSC), which manages a common pool of spectrum and is responsible for distributing the spectrum to individual ANs in a fair manner. Since the available spectrum is inadequate to satisfy the aggregate demand from all ANs simultaneously, LSC has to employ a dynamic spectrum allocation strategy. We have modeled the problem as an n-player cooperative bargaining game and have solved the problem with the help of solution techniques namely, Nash bargaining solution (NBS), Kalai-Smorodinsky bargaining solution (KSBS) and modified Thomson bargaining solution (MTBS). We have presented two novel heuristics to compute KSBS and MTBS. Moreover, a suitable utility function for the AN with respect to its received spectrum have been presented. We have also identified possible objectives of LSC (i.e., SP), namely minimizing overall dissatisfaction (MOD) of heterogeneous network, maintaining equality of distribution (MED) and maximizing proportional fairness (MPF). Finally, we have compared performances of above solutions with max–min fairness solution and Shapley value solution with respect to the above objectives of LSC.     

Derated Capacity States of a Large Thermal Generating Unit

Large thermal generation units operate under derated capacity conditions for considerable part of their up time as a result of the failure of its auxiliary units. The long term behaviour of such a unit is simulated by presenting a multilevel model, using state space methods. Truncation of the state space is adopted to reduce computation without introducing appreciable error.     

Going the distance with auxin: unravelling the molecular basis of auxin transport

Auxin represents one of the most important classes of signalling molecules described in plants. Auxins regulate several fundamental cellular processes including division, elongation and differentiation. Indole-3-acetic acid (IAA), the principal form of auxin in higher plants, is first synthesized within young apical tissues, then conveyed to its basal target tissues by a specialized delivery system termed polar auxin transport. The polarity of IAA movement represents one of the most novel aspect of auxin signalling. IAA transport has been demonstrated to involve auxin influx and efflux carrier activities. The adoption of a mutational approach in the model plant Arabidopsis thaliana has led to the identification of a number of genes which encode components for, or regulate the activity of, the auxin transport machinery. This paper will review the advances being made in identifying and characterizing these auxin transport-related gene products and discuss their importance within the context of Arabidopsis development.     

Spaceborne and airborne sensors in water quality assessment

Remotely sensed data can increase the ability of water resources researchers and decision-makers to monitor waterbodies more effectively. In recent decades, remote-sensing techniques have been widely used to measure the qualitative parameters of waterbodies (i.e. suspended sediments, coloured dissolved organic matter (CDOM), chlorophyll-a, and pollutants). A large number of different sensors on board various satellites and other platforms, such as aeroplanes, are currently used to measure the amount of radiation at various wavelengths reflected from the water’s surface. In this review article, the more commonly employed spaceborne and airborne sensors used in water quality investigations are introduced and their utility in the quantitative assessment of the quality parameters of waterbodies is discussed. Various properties (spectral, spatial and temporal, etc.) of spaceborne and airborne sensors are tabulated to be used as a sensor selection guide. These tables are helpful when designing water quality assessment studies, and can be used for the selection of appropriate sensors among many other available sensors in the market. Finally, based on the literature survey, a compilation of sensors used to measure various water quality parameters is presented.     

CoBi: Pattern Based Co-Regulated Biclustering of Gene Expression Data

Co-regulation is a common phenomenon in gene expression. Finding positively and negatively co-regulated gene clusters from gene expression data is a real need. Existing techniques based on global similarity are unable to detect true up- and down-regulated gene clusters. This paper presents an expression pattern based biclustering technique, CoBi, for grouping both positively and negatively regulated genes from microarray expression data. Regulation pattern and similarity in degree of fluctuation are accounted for while computing similarity between two genes. Unlike traditional biclustering techniques, which use greedy iterative approaches, it uses a BiClust tree that needs single pass over the entire dataset to find a set of biologically relevant biclusters. Biclusters determined from different gene expression datasets by the technique show highly enriched functional categories.     

Wireless Node Localization under Hostile Radio Environment using Smart Antenna

This paper presents a resilient localization scheme for wireless sensor networks (WSNs). It suits well in estimation of node position under a corrupted radio environment. Position computation is based on information of angle-of-arrivals (AoA) and references obtained from a few mobile anchors. In the network, anchors are equipped with smart antennas and global positioning system receivers. They broadcast signals in a synchronous and periodic fashion. The neighboring nodes having the signals with received signal strength values above a prescribed threshold level, respond with their respective IDs. Anchors evaluate AoA information from these signals using estimation of signal parameters via rotational invariance technique (ESPRIT) algorithm. Next, they forward beacon messages, containing their references and estimated angles, to the corresponding nodes and move along random trajectories. After receiving three sets of such data, at least, nodes can initiate selective segregation of the inconsistent position estimations. Simulation results attaining higher degree of localization accuracy validate its competency over the existing schemes.