Distributed sequential task allocation in foraging swarms

When designing a practical swarm robotics system, self-organized task allocation is key to make best use of resources. Current research in this area focuses on task allocation which is either distributed (tasks must be performed at different locations) or sequential (tasks are complex and must be split into simpler sub-tasks and processed in order). In practice, however, swarms will need to deal with tasks which are both distributed and sequential. In this paper, a classic foraging problem is extended to incorporate both distributed and sequential tasks. The problem is analysed theoretically, absolute limits on performance are derived, and a set of conditions for a successful algorithm are established. It is shown empirically that an algorithm which meets these conditions, by causing emergent cooperation between robots can achieve consistently high performance under a wide range of settings without the need for communication. © 2013 IEEE.

Solving Maximum Clique Problem for Protein Structure Similarity

Computing the similarity between two protein structures is a crucial task in molecular biology, and has been extensively investigated. Many protein structure comparison methods can be modeled as maximum weighted clique problems in specific k-partite graphs, referred here as alignment graphs. In this paper we present both a new integer programming formulation for solving such clique problems and a dedicated branch and bound algorithm for solving the maximum cardinality clique problem. Both approaches have been integrated in VAST, a software for aligning protein 3D structures largely used in the National Center for Biotechnology Information, an original clique solver which uses the well known Bron and Kerbosch algorithm (BK). Our computational results on real protein alignment instances show that our branch and bound algorithm is up to 116 times faster than BK.

Laser tracker position optimization

This article presents a laser tracker position optimization code based on the tracker uncertainty model developed by the National Physical Laboratory (NPL). The code is able to find the optimal tracker positions for generic measurements involving one or a network of many trackers, and an arbitrary set of targets. The optimization is performed using pattern search or optionally, genetic algorithm (GA) or particle swarm optimization (PSO). Different objective function weightings for the uncertainties of individual points, distance uncertainties between point pairs, and the angular uncertainties between three points can be defined. Constraints for tracker position limits and minimum measurement distances have also been implemented. Furthermore, position optimization taking into account of lines-of-sight (LOS) within complex CAD geometry have also been demonstrated. The code is simple to use and can be a valuable measurement planning tool.

Laser tracker position optimization

This article presents a laser tracker position optimization code based on the tracker uncertainty model developed by the National Physical Laboratory (NPL). The code is able to find the optimal tracker positions for generic measurements involving one or a network of many trackers, and an arbitrary set of targets. The optimization is performed using pattern search or optionally, genetic algorithm (GA) or particle swarm optimization (PSO). Different objective function weightings for the uncertainties of individual points, distance uncertainties between point pairs, and the angular uncertainties between three points can be defined. Constraints for tracker position limits and minimum measurement distances have also been implemented. Furthermore, position optimization taking into account of lines-of-sight (LOS) within complex CAD geometry have also been demonstrated. The code is simple to use and can be a valuable measurement planning tool.

A novel algorithm of posture best fit based on key characteristics for large components assembly

Measurement and variation control of geometrical Key Characteristics (KCs), such as flatness and gap of joint faces, coaxiality of cabin sections, is the crucial issue in large components assembly from the aerospace industry. Aiming to control geometrical KCs and to attain the best fit of posture, an optimization algorithm based on KCs for large components assembly is proposed. This approach regards the posture best fit, which is a key activity in Measurement Aided Assembly (MAA), as a two-phase optimal problem. In the first phase, the global measurement coordinate system of digital model and shop floor is unified with minimum error based on singular value decomposition, and the current posture of components being assembly is optimally solved in terms of minimum variation of all reference points. In the second phase, the best posture of the movable component is optimally determined by minimizing multiple KCs' variation with the constraints that every KC respectively conforms to its product specification. The optimal models and the process procedures for these two-phase optimal problems based on Particle Swarm Optimization (PSO) are proposed. In each model, every posture to be calculated is modeled as a 6 dimensional particle (three movement and three rotation parameters). Finally, an example that two cabin sections of satellite mainframe structure are being assembled is selected to verify the effectiveness of the proposed approach, models and algorithms. The experiment result shows the approach is promising and will provide a foundation for further study and application. © 2013 The Authors.

DFIG machine design for maximizing power output based on surrogate optimization algorithm

This paper presents a surrogate-model-based optimization of a doubly-fed induction generator (DFIG) machine winding design for maximizing power yield. Based on site-specific wind profile data and the machine's previous operational performance, the DFIG's stator and rotor windings are optimized to match the maximum efficiency with operating conditions for rewinding purposes. The particle swarm optimization-based surrogate optimization techniques are used in conjunction with the finite element method to optimize the machine design utilizing the limited available information for the site-specific wind profile and generator operating conditions. A response surface method in the surrogate model is developed to formulate the design objectives and constraints. Besides, the machine tests and efficiency calculations follow IEEE standard 112-B. Numerical and experimental results validate the effectiveness of the proposed technologies.

Design Optimization of Submerged Jet Nozzles for Enhanced Mixing

The purpose of this thesis was to identify the optimal design parameters for a jet nozzle which obtains a local maximum shear stress while maximizing the average shear stress on the floor of a fluid filled system. This research examined how geometric parameters of a jet nozzle, such as the nozzle's angle, height, and orifice, influence the shear stress created on the bottom surface of a tank. Simulations were run using a Computational Fluid Dynamics (CFD) software package to determine shear stress values for a parameterized geometric domain including the jet nozzle. A response surface was created based on the shear stress values obtained from 112 simulated designs. A multi-objective optimization software utilized the response surface to generate designs with the best combination of parameters to achieve maximum shear stress and maximum average shear stress. The optimal configuration of parameters achieved larger shear stress values over a commercially available design.

Progress towards Elimination of HIV Mother-to-Child Transmission in the Dominican Republic from 1999 to 2011

In 1999, prevention of mother-to-child transmission (pMTCT) using antiretrovirals was introduced in the Dominican Republic (DR). Highly active antiretroviral therapy (HAART) was introduced for immunosuppressed persons in 2004 and for pMTCT in 2008. To assess progress towards MTCT elimination, data from requisitions for HIV nucleic acid amplification tests for diagnosis of HIV infection in perinatally exposed infants born in the DR from 1999 to 2011 were analyzed. The MTCT rate was 142/1,274 (11.1%) in 1999–2008 and 12/302 (4.0%) in 2009–2011 (), with a rate of 154/1,576 (9.8%) for both periods combined. This decline was associated with significant increases in the proportions of women who received prenatal HAART (from 12.3% to 67.9%) and infants who received exclusive formula feeding (from 76.3% to 86.1%) and declines in proportions of women who received no prenatal antiretrovirals (from 31.9% to 12.2%) or received only single-dose nevirapine (from 39.5% to 19.5%). In 2007, over 95% of DR pregnant women received prenatal care, HIV testing, and professionally attended delivery. However, only 58% of women in underserved sugarcane plantation communities (2007) and 76% in HIV sentinel surveillance hospitals (2003–2005) received their HIV test results. HIV-MTCT elimination is feasible but persistent lack of access to critical pMTCT measures must be addressed.

Grey Slate

GREY SLATE is a collection of poems that focuses on the natural world in order to explore the mysteries of life with the intent to create a meditation on what it means to be a human being interacting with this world. Inspired by John Keats’ theory of Negative Capability, GREY SLATE does not seek to explain, but to dwell in the mysteries it explores. The poems are tied together through similar images or ideas in order to mimic the way the mind works as it jumps from thought to thought. GREY SLATE also mixes different types of poems: from haiku to sonnet to paradelle, and from lyric to narrative to prose poem. GREY SLATE hopes to inspire readers to take a break from searching for truths and indulge in the beautiful mystery that is life with no need for answers.

Blue Development: Analyzing the Role of the State in the Rise of Aquaculture in India

In this paper, I analyze the processes that transformed aquaculture into a major export industry in India, in order to understand the role aquaculture has as a social and economic development strategy of the Indian state. The discussion employs a political ecology approach as I explore the complex relations between access and control of India’s coastal resources, society, and economy. I argue that many of the forces that initiated the development of aquaculture in India, namely the involvement of the Indian state, continue to shape the industry today. I also discuss how despite widespread social conflict, the shrimp farming industry, in particular, continues to thrive and grow in rural India. My analysis utilizes ethnographic and archival data collected over the course of 9 months of fieldwork in Tamil Nadu, India. Keywords: rural development, aquaculture, India, State, ethnography

Understanding the role of Dimethylsulfoniopropionate as a potential chemotactic cue for coral-associated bacteria

Most reef-building corals are known to engage in non-pathogenic symbiosis not only with unicellular dinoflagellates from the genus Symbiodinium, but also with other microscopic organisms such as bacteria, fungi, and viruses. The functional details of these highly complex associations remain largely unclear. The impetus of this study is to gain a better understanding of the symbiotic interaction between marine bacteria and their coral host. Studies have shown that certain bacterial orders associate with specific certain coral species, thus making the symbiotic synergy a non-random consortium. Consequently both corals and bacteria may be capable of emitting chemical cues that enable both parties to find one another and thus generate the symbiosis. The production of these cues by the symbionts may be the result of environmental stimuli such as elevated ocean temperatures, increased water acidity, and even predation. One potential chemical cue could be the compound DMSP (Dimethylsulfoniopropionate) and its sulphur derivatives. Reef-building corals are believed to be the major producers of the DMSP during times of stress. Marine bacteria utilize DMSP as a source of sulfur and carbon. As a result corals could potentially attract their bacterial consortium depending on their DMSP production. This would enable them to adapt to fluctuating environmental conditions by changing their bacterial communities to that which may aid in survival. To test the hypothesis that coral-produced DMSP plays a role in attracting symbiotic bacteria, this study utilized the advent of high-throughput sequencing paired with chemotactic assays to determine the response of coral-associated bacterial isolates towards the DMSP compound at differing concentrations. Chemotaxis assays revealed that some isolates responded positively towards the DMSP compound. This finding adds to existing evidence suggesting that coral-associated pathogens utilize chemotaxis as a host colonization and detection mechanism. Thus the symbiotic bacteria that make up the coral microbiome may also employ this process. Furthermore this study demonstrates that bacterial motility may be a strong contributing factor in the response to the chemotactic cue. Swarming motility may be better suited for bacteria that need to respond to a chemical gradient on the surface of the coral. Therefore the isolates that were able to swarm seemed to respond more strongly to the DMSP.

Modified continuous ant colony algorithm for function optimization

Many classical as well as modern optimization techniques exist. One such modern method belonging to the field of swarm intelligence is termed ant colony optimization. This relatively new concept in optimization involves the use of artificial ants and is based on real ant behavior inspired by the way ants search for food. In this thesis, a novel ant colony optimization technique for continuous domains was developed. The goal was to provide improvements in computing time and robustness when compared to other optimization algorithms. Optimization function spaces can have extreme topologies and are therefore difficult to optimize. The proposed method effectively searched the domain and solved difficult single-objective optimization problems. The developed algorithm was run for numerous classic test cases for both single and multi-objective problems. The results demonstrate that the method is robust, stable, and that the number of objective function evaluations is comparable to other optimization algorithms.

Optimization of three-dimensional branching networks of microchannels for thermal management of microelectronics

The aim of this work is to present a methodology to develop cost-effective thermal management solutions for microelectronic devices, capable of removing maximum amount of heat and delivering maximally uniform temperature distributions. The topological and geometrical characteristics of multiple-story three-dimensional branching networks of microchannels were developed using multi-objective optimization. A conjugate heat transfer analysis software package and an automatic 3D microchannel network generator were developed and coupled with a modified version of a particle-swarm optimization algorithm with a goal of creating a design tool for 3D networks of optimized coolant flow passages. Numerical algorithms in the conjugate heat transfer solution package include a quasi-ID thermo-fluid solver and a steady heat diffusion solver, which were validated against results from high-fidelity Navier-Stokes equations solver and analytical solutions for basic fluid dynamics test cases. Pareto-optimal solutions demonstrate that thermal loads of up to 500 W/cm2 can be managed with 3D microchannel networks, with pumping power requirements up to 50% lower with respect to currently used high-performance cooling technologies.