Joint optimization of maintenance, buffers and machines in manufacturing lines

NASA Astrophysics Data System (ADS)

Nahas, Nabil; Nourelfath, Mustapha

2018-01-01

This article considers a series manufacturing line composed of several machines separated by intermediate buffers of finite capacity. The goal is to find the optimal number of preventive maintenance actions performed on each machine, the optimal selection of machines and the optimal buffer allocation plan that minimize the total system cost, while providing the desired system throughput level. The mean times between failures of all machines are assumed to increase when applying periodic preventive maintenance. To estimate the production line throughput, a decomposition method is used. The decision variables in the formulated optimal design problem are buffer levels, types of machines and times between preventive maintenance actions. Three heuristic approaches are developed to solve the formulated combinatorial optimization problem. The first heuristic consists of a genetic algorithm, the second is based on the nonlinear threshold accepting metaheuristic and the third is an ant colony system. The proposed heuristics are compared and their efficiency is shown through several numerical examples. It is found that the nonlinear threshold accepting algorithm outperforms the genetic algorithm and ant colony system, while the genetic algorithm provides better results than the ant colony system for longer manufacturing lines.

Application of the artificial bee colony algorithm for solving the set covering problem.

PubMed

Crawford, Broderick; Soto, Ricardo; Cuesta, Rodrigo; Paredes, Fernando

2014-01-01

The set covering problem is a formal model for many practical optimization problems. In the set covering problem the goal is to choose a subset of the columns of minimal cost that covers every row. Here, we present a novel application of the artificial bee colony algorithm to solve the non-unicost set covering problem. The artificial bee colony algorithm is a recent swarm metaheuristic technique based on the intelligent foraging behavior of honey bees. Experimental results show that our artificial bee colony algorithm is competitive in terms of solution quality with other recent metaheuristic approaches for the set covering problem.

Application of the Artificial Bee Colony Algorithm for Solving the Set Covering Problem

PubMed Central

Crawford, Broderick; Soto, Ricardo; Cuesta, Rodrigo; Paredes, Fernando

2014-01-01

The set covering problem is a formal model for many practical optimization problems. In the set covering problem the goal is to choose a subset of the columns of minimal cost that covers every row. Here, we present a novel application of the artificial bee colony algorithm to solve the non-unicost set covering problem. The artificial bee colony algorithm is a recent swarm metaheuristic technique based on the intelligent foraging behavior of honey bees. Experimental results show that our artificial bee colony algorithm is competitive in terms of solution quality with other recent metaheuristic approaches for the set covering problem. PMID:24883356

Estimation method for serial dilution experiments.

PubMed

Ben-David, Avishai; Davidson, Charles E

2014-12-01

Titration of microorganisms in infectious or environmental samples is a corner stone of quantitative microbiology. A simple method is presented to estimate the microbial counts obtained with the serial dilution technique for microorganisms that can grow on bacteriological media and develop into a colony. The number (concentration) of viable microbial organisms is estimated from a single dilution plate (assay) without a need for replicate plates. Our method selects the best agar plate with which to estimate the microbial counts, and takes into account the colony size and plate area that both contribute to the likelihood of miscounting the number of colonies on a plate. The estimate of the optimal count given by our method can be used to narrow the search for the best (optimal) dilution plate and saves time. The required inputs are the plate size, the microbial colony size, and the serial dilution factors. The proposed approach shows relative accuracy well within ±0.1log10 from data produced by computer simulations. The method maintains this accuracy even in the presence of dilution errors of up to 10% (for both the aliquot and diluent volumes), microbial counts between 10(4) and 10(12) colony-forming units, dilution ratios from 2 to 100, and plate size to colony size ratios between 6.25 to 200. Published by Elsevier B.V.

An Improved Artificial Bee Colony-Based Approach for Zoning Protected Ecological Areas

PubMed Central

Shao, Jing; Yang, Lina; Peng, Ling; Chi, Tianhe; Wang, Xiaomeng

2015-01-01

China is facing ecological and environmental challenges as its urban growth rate continues to rise, and zoning protected ecological areas is recognized as an effective response measure. Zoning inherently involves both site attributes and aggregation attributes, and the combination of mathematical models and heuristic algorithms have proven advantageous. In this article, an improved artificial bee colony (IABC)-based approach is proposed for zoning protected ecological areas at a regional scale. Three main improvements were made: the first is the use of multiple strategies to generate the initial bee population of a specific quality and diversity, the second is an exploitation search procedure to generate neighbor solutions combining “replace” and “alter” operations, and the third is a “swap” strategy to enable a local search for the iterative optimal solution. The IABC algorithm was verified using simulated data. Then it was applied to define an optimum scheme of protected ecological areas of Sanya (in the Hainan province of China), and a reasonable solution was obtained. Finally, a comparison experiment with other methods (agent-based land allocation model, ant colony optimization, and density slicing) was conducted and demonstrated that the IABC algorithm was more effective and efficient than the other methods. Through this study, we aimed to provide a scientifically sound, practical approach for zoning procedures. PMID:26394148

Optimal management of substrates in anaerobic co-digestion: An ant colony algorithm approach.

PubMed

Verdaguer, Marta; Molinos-Senante, María; Poch, Manel

2016-04-01

Sewage sludge (SWS) is inevitably produced in urban wastewater treatment plants (WWTPs). The treatment of SWS on site at small WWTPs is not economical; therefore, the SWS is typically transported to an alternative SWS treatment center. There is increased interest in the use of anaerobic digestion (AnD) with co-digestion as an SWS treatment alternative. Although the availability of different co-substrates has been ignored in most of the previous studies, it is an essential issue for the optimization of AnD co-digestion. In a pioneering approach, this paper applies an Ant-Colony-Optimization (ACO) algorithm that maximizes the generation of biogas through AnD co-digestion in order to optimize the discharge of organic waste from different waste sources in real-time. An empirical application is developed based on a virtual case study that involves organic waste from urban WWTPs and agrifood activities. The results illustrate the dominate role of toxicity levels in selecting contributions to the AnD input. The methodology and case study proposed in this paper demonstrate the usefulness of the ACO approach in supporting a decision process that contributes to improving the sustainability of organic waste and SWS management. Copyright © 2016 Elsevier Ltd. All rights reserved.

Warehouse stocking optimization based on dynamic ant colony genetic algorithm

NASA Astrophysics Data System (ADS)

Xiao, Xiaoxu

2018-04-01

In view of the various orders of FAW (First Automotive Works) International Logistics Co., Ltd., the SLP method is used to optimize the layout of the warehousing units in the enterprise, thus the warehouse logistics is optimized and the external processing speed of the order is improved. In addition, the relevant intelligent algorithms for optimizing the stocking route problem are analyzed. The ant colony algorithm and genetic algorithm which have good applicability are emphatically studied. The parameters of ant colony algorithm are optimized by genetic algorithm, which improves the performance of ant colony algorithm. A typical path optimization problem model is taken as an example to prove the effectiveness of parameter optimization.

Model Specification Searches Using Ant Colony Optimization Algorithms

ERIC Educational Resources Information Center

Marcoulides, George A.; Drezner, Zvi

2003-01-01

Ant colony optimization is a recently proposed heuristic procedure inspired by the behavior of real ants. This article applies the procedure to model specification searches in structural equation modeling and reports the results. The results demonstrate the capabilities of ant colony optimization algorithms for conducting automated searches.

Production scheduling with ant colony optimization

NASA Astrophysics Data System (ADS)

Chernigovskiy, A. S.; Kapulin, D. V.; Noskova, E. E.; Yamskikh, T. N.; Tsarev, R. Yu

2017-10-01

The optimum solution of the production scheduling problem for manufacturing processes at an enterprise is crucial as it allows one to obtain the required amount of production within a specified time frame. Optimum production schedule can be found using a variety of optimization algorithms or scheduling algorithms. Ant colony optimization is one of well-known techniques to solve the global multi-objective optimization problem. In the article, the authors present a solution of the production scheduling problem by means of an ant colony optimization algorithm. A case study of the algorithm efficiency estimated against some others production scheduling algorithms is presented. Advantages of the ant colony optimization algorithm and its beneficial effect on the manufacturing process are provided.

Foraging on the potential energy surface: a swarm intelligence-based optimizer for molecular geometry.

PubMed

Wehmeyer, Christoph; Falk von Rudorff, Guido; Wolf, Sebastian; Kabbe, Gabriel; Schärf, Daniel; Kühne, Thomas D; Sebastiani, Daniel

2012-11-21

We present a stochastic, swarm intelligence-based optimization algorithm for the prediction of global minima on potential energy surfaces of molecular cluster structures. Our optimization approach is a modification of the artificial bee colony (ABC) algorithm which is inspired by the foraging behavior of honey bees. We apply our modified ABC algorithm to the problem of global geometry optimization of molecular cluster structures and show its performance for clusters with 2-57 particles and different interatomic interaction potentials.

Foraging on the potential energy surface: A swarm intelligence-based optimizer for molecular geometry

NASA Astrophysics Data System (ADS)

Wehmeyer, Christoph; Falk von Rudorff, Guido; Wolf, Sebastian; Kabbe, Gabriel; Schärf, Daniel; Kühne, Thomas D.; Sebastiani, Daniel

2012-11-01

We present a stochastic, swarm intelligence-based optimization algorithm for the prediction of global minima on potential energy surfaces of molecular cluster structures. Our optimization approach is a modification of the artificial bee colony (ABC) algorithm which is inspired by the foraging behavior of honey bees. We apply our modified ABC algorithm to the problem of global geometry optimization of molecular cluster structures and show its performance for clusters with 2-57 particles and different interatomic interaction potentials.

On the problem of solving the optimization for continuous space based on information distribution function of ant colony algorithm

NASA Astrophysics Data System (ADS)

Min, Huang; Na, Cai

2017-06-01

These years, ant colony algorithm has been widely used in solving the domain of discrete space optimization, while the research on solving the continuous space optimization was relatively little. Based on the original optimization for continuous space, the article proposes the improved ant colony algorithm which is used to Solve the optimization for continuous space, so as to overcome the ant colony algorithm’s disadvantages of searching for a long time in continuous space. The article improves the solving way for the total amount of information of each interval and the due number of ants. The article also introduces a function of changes with the increase of the number of iterations in order to enhance the convergence rate of the improved ant colony algorithm. The simulation results show that compared with the result in literature[5], the suggested improved ant colony algorithm that based on the information distribution function has a better convergence performance. Thus, the article provides a new feasible and effective method for ant colony algorithm to solve this kind of problem.

Ant system: optimization by a colony of cooperating agents.

PubMed

Dorigo, M; Maniezzo, V; Colorni, A

1996-01-01

An analogy with the way ant colonies function has suggested the definition of a new computational paradigm, which we call ant system (AS). We propose it as a viable new approach to stochastic combinatorial optimization. The main characteristics of this model are positive feedback, distributed computation, and the use of a constructive greedy heuristic. Positive feedback accounts for rapid discovery of good solutions, distributed computation avoids premature convergence, and the greedy heuristic helps find acceptable solutions in the early stages of the search process. We apply the proposed methodology to the classical traveling salesman problem (TSP), and report simulation results. We also discuss parameter selection and the early setups of the model, and compare it with tabu search and simulated annealing using TSP. To demonstrate the robustness of the approach, we show how the ant system (AS) can be applied to other optimization problems like the asymmetric traveling salesman, the quadratic assignment and the job-shop scheduling. Finally we discuss the salient characteristics-global data structure revision, distributed communication and probabilistic transitions of the AS.

Meta-Heuristics in Short Scale Construction: Ant Colony Optimization and Genetic Algorithm.

PubMed

Schroeders, Ulrich; Wilhelm, Oliver; Olaru, Gabriel

2016-01-01

The advent of large-scale assessment, but also the more frequent use of longitudinal and multivariate approaches to measurement in psychological, educational, and sociological research, caused an increased demand for psychometrically sound short scales. Shortening scales economizes on valuable administration time, but might result in inadequate measures because reducing an item set could: a) change the internal structure of the measure, b) result in poorer reliability and measurement precision, c) deliver measures that cannot effectively discriminate between persons on the intended ability spectrum, and d) reduce test-criterion relations. Different approaches to abbreviate measures fare differently with respect to the above-mentioned problems. Therefore, we compare the quality and efficiency of three item selection strategies to derive short scales from an existing long version: a Stepwise COnfirmatory Factor Analytical approach (SCOFA) that maximizes factor loadings and two metaheuristics, specifically an Ant Colony Optimization (ACO) with a tailored user-defined optimization function and a Genetic Algorithm (GA) with an unspecific cost-reduction function. SCOFA compiled short versions were highly reliable, but had poor validity. In contrast, both metaheuristics outperformed SCOFA and produced efficient and psychometrically sound short versions (unidimensional, reliable, sensitive, and valid). We discuss under which circumstances ACO and GA produce equivalent results and provide recommendations for conditions in which it is advisable to use a metaheuristic with an unspecific out-of-the-box optimization function.

Meta-Heuristics in Short Scale Construction: Ant Colony Optimization and Genetic Algorithm

PubMed Central

Schroeders, Ulrich; Wilhelm, Oliver; Olaru, Gabriel

2016-01-01

The advent of large-scale assessment, but also the more frequent use of longitudinal and multivariate approaches to measurement in psychological, educational, and sociological research, caused an increased demand for psychometrically sound short scales. Shortening scales economizes on valuable administration time, but might result in inadequate measures because reducing an item set could: a) change the internal structure of the measure, b) result in poorer reliability and measurement precision, c) deliver measures that cannot effectively discriminate between persons on the intended ability spectrum, and d) reduce test-criterion relations. Different approaches to abbreviate measures fare differently with respect to the above-mentioned problems. Therefore, we compare the quality and efficiency of three item selection strategies to derive short scales from an existing long version: a Stepwise COnfirmatory Factor Analytical approach (SCOFA) that maximizes factor loadings and two metaheuristics, specifically an Ant Colony Optimization (ACO) with a tailored user-defined optimization function and a Genetic Algorithm (GA) with an unspecific cost-reduction function. SCOFA compiled short versions were highly reliable, but had poor validity. In contrast, both metaheuristics outperformed SCOFA and produced efficient and psychometrically sound short versions (unidimensional, reliable, sensitive, and valid). We discuss under which circumstances ACO and GA produce equivalent results and provide recommendations for conditions in which it is advisable to use a metaheuristic with an unspecific out-of-the-box optimization function. PMID:27893845

Adapting an Ant Colony Metaphor for Multi-Robot Chemical Plume Tracing

PubMed Central

Meng, Qing-Hao; Yang, Wei-Xing; Wang, Yang; Li, Fei; Zeng, Ming

2012-01-01

We consider chemical plume tracing (CPT) in time-varying airflow environments using multiple mobile robots. The purpose of CPT is to approach a gas source with a previously unknown location in a given area. Therefore, the CPT could be considered as a dynamic optimization problem in continuous domains. The traditional ant colony optimization (ACO) algorithm has been successfully used for combinatorial optimization problems in discrete domains. To adapt the ant colony metaphor to the multi-robot CPT problem, the two-dimension continuous search area is discretized into grids and the virtual pheromone is updated according to both the gas concentration and wind information. To prevent the adapted ACO algorithm from being prematurely trapped in a local optimum, the upwind surge behavior is adopted by the robots with relatively higher gas concentration in order to explore more areas. The spiral surge (SS) algorithm is also examined for comparison. Experimental results using multiple real robots in two indoor natural ventilated airflow environments show that the proposed CPT method performs better than the SS algorithm. The simulation results for large-scale advection-diffusion plume environments show that the proposed method could also work in outdoor meandering plume environments. PMID:22666056

Adapting an ant colony metaphor for multi-robot chemical plume tracing.

PubMed

Meng, Qing-Hao; Yang, Wei-Xing; Wang, Yang; Li, Fei; Zeng, Ming

2012-01-01

We consider chemical plume tracing (CPT) in time-varying airflow environments using multiple mobile robots. The purpose of CPT is to approach a gas source with a previously unknown location in a given area. Therefore, the CPT could be considered as a dynamic optimization problem in continuous domains. The traditional ant colony optimization (ACO) algorithm has been successfully used for combinatorial optimization problems in discrete domains. To adapt the ant colony metaphor to the multi-robot CPT problem, the two-dimension continuous search area is discretized into grids and the virtual pheromone is updated according to both the gas concentration and wind information. To prevent the adapted ACO algorithm from being prematurely trapped in a local optimum, the upwind surge behavior is adopted by the robots with relatively higher gas concentration in order to explore more areas. The spiral surge (SS) algorithm is also examined for comparison. Experimental results using multiple real robots in two indoor natural ventilated airflow environments show that the proposed CPT method performs better than the SS algorithm. The simulation results for large-scale advection-diffusion plume environments show that the proposed method could also work in outdoor meandering plume environments.

A Modified Mean Gray Wolf Optimization Approach for Benchmark and Biomedical Problems.

PubMed

Singh, Narinder; Singh, S B

2017-01-01

A modified variant of gray wolf optimization algorithm, namely, mean gray wolf optimization algorithm has been developed by modifying the position update (encircling behavior) equations of gray wolf optimization algorithm. The proposed variant has been tested on 23 standard benchmark well-known test functions (unimodal, multimodal, and fixed-dimension multimodal), and the performance of modified variant has been compared with particle swarm optimization and gray wolf optimization. Proposed algorithm has also been applied to the classification of 5 data sets to check feasibility of the modified variant. The results obtained are compared with many other meta-heuristic approaches, ie, gray wolf optimization, particle swarm optimization, population-based incremental learning, ant colony optimization, etc. The results show that the performance of modified variant is able to find best solutions in terms of high level of accuracy in classification and improved local optima avoidance.

A new collaborative recommendation approach based on users clustering using artificial bee colony algorithm.

PubMed

Ju, Chunhua; Xu, Chonghuan

2013-01-01

Although there are many good collaborative recommendation methods, it is still a challenge to increase the accuracy and diversity of these methods to fulfill users' preferences. In this paper, we propose a novel collaborative filtering recommendation approach based on K-means clustering algorithm. In the process of clustering, we use artificial bee colony (ABC) algorithm to overcome the local optimal problem caused by K-means. After that we adopt the modified cosine similarity to compute the similarity between users in the same clusters. Finally, we generate recommendation results for the corresponding target users. Detailed numerical analysis on a benchmark dataset MovieLens and a real-world dataset indicates that our new collaborative filtering approach based on users clustering algorithm outperforms many other recommendation methods.

A New Collaborative Recommendation Approach Based on Users Clustering Using Artificial Bee Colony Algorithm

PubMed Central

Ju, Chunhua

2013-01-01

Although there are many good collaborative recommendation methods, it is still a challenge to increase the accuracy and diversity of these methods to fulfill users' preferences. In this paper, we propose a novel collaborative filtering recommendation approach based on K-means clustering algorithm. In the process of clustering, we use artificial bee colony (ABC) algorithm to overcome the local optimal problem caused by K-means. After that we adopt the modified cosine similarity to compute the similarity between users in the same clusters. Finally, we generate recommendation results for the corresponding target users. Detailed numerical analysis on a benchmark dataset MovieLens and a real-world dataset indicates that our new collaborative filtering approach based on users clustering algorithm outperforms many other recommendation methods. PMID:24381525

A framework for using ant colony optimization to schedule environmental flow management alternatives for rivers, wetlands, and floodplains

NASA Astrophysics Data System (ADS)

Szemis, J. M.; Maier, H. R.; Dandy, G. C.

2012-08-01

Rivers, wetlands, and floodplains are in need of management as they have been altered from natural conditions and are at risk of vanishing because of river development. One method to mitigate these impacts involves the scheduling of environmental flow management alternatives (EFMA); however, this is a complex task as there are generally a large number of ecological assets (e.g., wetlands) that need to be considered, each with species with competing flow requirements. Hence, this problem evolves into an optimization problem to maximize an ecological benefit within constraints imposed by human needs and the physical layout of the system. This paper presents a novel optimization framework which uses ant colony optimization to enable optimal scheduling of EFMAs, given constraints on the environmental water that is available. This optimization algorithm is selected because, unlike other currently popular algorithms, it is able to account for all aspects of the problem. The approach is validated by comparing it to a heuristic approach, and its utility is demonstrated using a case study based on the Murray River in South Australia to investigate (1) the trade-off between plant recruitment (i.e., promoting germination) and maintenance (i.e., maintaining habitat) flow requirements, (2) the trade-off between flora and fauna flow requirements, and (3) a hydrograph inversion case. The results demonstrate the usefulness and flexibility of the proposed framework as it is able to determine EFMA schedules that provide optimal or near-optimal trade-offs between the competing needs of species under a range of operating conditions and valuable insight for managers.

Improved Ant Algorithms for Software Testing Cases Generation

PubMed Central

Yang, Shunkun; Xu, Jiaqi

2014-01-01

Existing ant colony optimization (ACO) for software testing cases generation is a very popular domain in software testing engineering. However, the traditional ACO has flaws, as early search pheromone is relatively scarce, search efficiency is low, search model is too simple, positive feedback mechanism is easy to porduce the phenomenon of stagnation and precocity. This paper introduces improved ACO for software testing cases generation: improved local pheromone update strategy for ant colony optimization, improved pheromone volatilization coefficient for ant colony optimization (IPVACO), and improved the global path pheromone update strategy for ant colony optimization (IGPACO). At last, we put forward a comprehensive improved ant colony optimization (ACIACO), which is based on all the above three methods. The proposed technique will be compared with random algorithm (RND) and genetic algorithm (GA) in terms of both efficiency and coverage. The results indicate that the improved method can effectively improve the search efficiency, restrain precocity, promote case coverage, and reduce the number of iterations. PMID:24883391

Life Span Evolution in Eusocial Workers—A Theoretical Approach to Understanding the Effects of Extrinsic Mortality in a Hierarchical System

PubMed Central

Kramer, Boris H.; Schaible, Ralf

2013-01-01

While the extraordinary life span of queens and division of labor in eusocial societies have been well studied, it is less clear which selective forces act on the short life span of workers. The disparity of life span between the queen and the workers is linked to a basic issue in sociobiology: How are the resources in a colony allocated between colony maintenance and reproduction? Resources for somatic maintenance of the colony can either be invested into quality or quantity of workers. Here, we present a theoretical optimization model that uses a hierarchical trade-off within insect colonies and extrinsic mortality to explain how different aging phenotypes could have evolved to keep resources secure in the colony. The model points to the significance of two factors. First, any investment that would generate a longer intrinsic life span for workers is lost if the individual dies from external causes while foraging. As a consequence, risky environments favor the evolution of workers with a shorter life span. Second, shorter-lived workers require less investment than long-lived ones, allowing the colony to allocate these resources to sexual reproduction or colony growth. PMID:23596527

Multi Dimensional Honey Bee Foraging Algorithm Based on Optimal Energy Consumption

NASA Astrophysics Data System (ADS)

Saritha, R.; Vinod Chandra, S. S.

2017-10-01

In this paper a new nature inspired algorithm is proposed based on natural foraging behavior of multi-dimensional honey bee colonies. This method handles issues that arise when food is shared from multiple sources by multiple swarms at multiple destinations. The self organizing nature of natural honey bee swarms in multiple colonies is based on the principle of energy consumption. Swarms of multiple colonies select a food source to optimally fulfill the requirements of its colonies. This is based on the energy requirement for transporting food between a source and destination. Minimum use of energy leads to maximizing profit in each colony. The mathematical model proposed here is based on this principle. This has been successfully evaluated by applying it on multi-objective transportation problem for optimizing cost and time. The algorithm optimizes the needs at each destination in linear time.

Application of ant colony optimization to optimal foragaing theory: comparison of simulation and field results

USDA-ARS?s Scientific Manuscript database

Ant Colony Optimization (ACO) refers to the family of algorithms inspired by the behavior of real ants and used to solve combinatorial problems such as the Traveling Salesman Problem (TSP).Optimal Foraging Theory (OFT) is an evolutionary principle wherein foraging organisms or insect parasites seek ...

Improved discrete swarm intelligence algorithms for endmember extraction from hyperspectral remote sensing images

NASA Astrophysics Data System (ADS)

Su, Yuanchao; Sun, Xu; Gao, Lianru; Li, Jun; Zhang, Bing

2016-10-01

Endmember extraction is a key step in hyperspectral unmixing. A new endmember extraction framework is proposed for hyperspectral endmember extraction. The proposed approach is based on the swarm intelligence (SI) algorithm, where discretization is used to solve the SI algorithm because pixels in a hyperspectral image are naturally defined within a discrete space. Moreover, a "distance" factor is introduced into the objective function to limit the endmember numbers which is generally limited in real scenarios, while traditional SI algorithms likely produce superabundant spectral signatures, which generally belong to the same classes. Three endmember extraction methods are proposed based on the artificial bee colony, ant colony optimization, and particle swarm optimization algorithms. Experiments with both simulated and real hyperspectral images indicate that the proposed framework can improve the accuracy of endmember extraction.

Discover for Yourself: An Optimal Control Model in Insect Colonies

ERIC Educational Resources Information Center

Winkel, Brian

2013-01-01

We describe the enlightening path of self-discovery afforded to the teacher of undergraduate mathematics. This is demonstrated as we find and develop background material on an application of optimal control theory to model the evolutionary strategy of an insect colony to produce the maximum number of queen or reproducer insects in the colony at…

A Hybrid Ant Colony Optimization Algorithm for the Extended Capacitated Arc Routing Problem.

PubMed

Li-Ning Xing; Rohlfshagen, P; Ying-Wu Chen; Xin Yao

2011-08-01

The capacitated arc routing problem (CARP) is representative of numerous practical applications, and in order to widen its scope, we consider an extended version of this problem that entails both total service time and fixed investment costs. We subsequently propose a hybrid ant colony optimization (ACO) algorithm (HACOA) to solve instances of the extended CARP. This approach is characterized by the exploitation of heuristic information, adaptive parameters, and local optimization techniques: Two kinds of heuristic information, arc cluster information and arc priority information, are obtained continuously from the solutions sampled to guide the subsequent optimization process. The adaptive parameters ease the burden of choosing initial values and facilitate improved and more robust results. Finally, local optimization, based on the two-opt heuristic, is employed to improve the overall performance of the proposed algorithm. The resulting HACOA is tested on four sets of benchmark problems containing a total of 87 instances with up to 140 nodes and 380 arcs. In order to evaluate the effectiveness of the proposed method, some existing capacitated arc routing heuristics are extended to cope with the extended version of this problem; the experimental results indicate that the proposed ACO method outperforms these heuristics.

Optimal time--energy allocation and the evolution of colony demography among eusocial insects. [Polistes fuscatus, Vespa orientalis, ants

DOE Office of Scientific and Technical Information (OSTI.GOV)

Macevicz, S.C.

1979-05-09

This thesis attempts to explain the evolution of certain features of social insect colony population structure by the use of optimization models. Two areas are examined in detail. First, the optimal reproductive strategies of annual eusocial insects are considered. A model is constructed for the growth of workers and reproductives as a function of the resources allocated to each. Next the allocation schedule is computed which yields the maximum number of reproductives by season's end. The results indicate that if there is constant return to scale for allocated resources the optimal strategy is to invest in colony growth until approximatelymore » one generation before season's end, whereupon worker production ceases and reproductive effort is switched entirely to producing queens and males. Furthermore, the results indicate that if there is decreasing return to scale for allocated resources then simultaneous production of workers and reproductives is possible. The model is used to explain the colony demography of two species of wasp, Polistes fuscatus and Vespa orientalis. Colonies of these insects undergo a sudden switch from the production of workers to the production of reproductives. The second area examined concerns optimal forager size distributions for monomorphic ant colonies. A model is constructed that describes the colony's energetic profit as a function which depends on the size distribution of food resources as well as forager efficiency, metabolic costs, and manufacturing costs.« less

Research on cutting path optimization of sheet metal parts based on ant colony algorithm

NASA Astrophysics Data System (ADS)

Wu, Z. Y.; Ling, H.; Li, L.; Wu, L. H.; Liu, N. B.

2017-09-01

In view of the disadvantages of the current cutting path optimization methods of sheet metal parts, a new method based on ant colony algorithm was proposed in this paper. The cutting path optimization problem of sheet metal parts was taken as the research object. The essence and optimization goal of the optimization problem were presented. The traditional serial cutting constraint rule was improved. The cutting constraint rule with cross cutting was proposed. The contour lines of parts were discretized and the mathematical model of cutting path optimization was established. Thus the problem was converted into the selection problem of contour lines of parts. Ant colony algorithm was used to solve the problem. The principle and steps of the algorithm were analyzed.

An improved ant colony optimization algorithm with fault tolerance for job scheduling in grid computing systems

PubMed Central

Idris, Hajara; Junaidu, Sahalu B.; Adewumi, Aderemi O.

2017-01-01

The Grid scheduler, schedules user jobs on the best available resource in terms of resource characteristics by optimizing job execution time. Resource failure in Grid is no longer an exception but a regular occurring event as resources are increasingly being used by the scientific community to solve computationally intensive problems which typically run for days or even months. It is therefore absolutely essential that these long-running applications are able to tolerate failures and avoid re-computations from scratch after resource failure has occurred, to satisfy the user’s Quality of Service (QoS) requirement. Job Scheduling with Fault Tolerance in Grid Computing using Ant Colony Optimization is proposed to ensure that jobs are executed successfully even when resource failure has occurred. The technique employed in this paper, is the use of resource failure rate, as well as checkpoint-based roll back recovery strategy. Check-pointing aims at reducing the amount of work that is lost upon failure of the system by immediately saving the state of the system. A comparison of the proposed approach with an existing Ant Colony Optimization (ACO) algorithm is discussed. The experimental results of the implemented Fault Tolerance scheduling algorithm show that there is an improvement in the user’s QoS requirement over the existing ACO algorithm, which has no fault tolerance integrated in it. The performance evaluation of the two algorithms was measured in terms of the three main scheduling performance metrics: makespan, throughput and average turnaround time. PMID:28545075

DyHAP: Dynamic Hybrid ANFIS-PSO Approach for Predicting Mobile Malware.

PubMed

Afifi, Firdaus; Anuar, Nor Badrul; Shamshirband, Shahaboddin; Choo, Kim-Kwang Raymond

2016-01-01

To deal with the large number of malicious mobile applications (e.g. mobile malware), a number of malware detection systems have been proposed in the literature. In this paper, we propose a hybrid method to find the optimum parameters that can be used to facilitate mobile malware identification. We also present a multi agent system architecture comprising three system agents (i.e. sniffer, extraction and selection agent) to capture and manage the pcap file for data preparation phase. In our hybrid approach, we combine an adaptive neuro fuzzy inference system (ANFIS) and particle swarm optimization (PSO). Evaluations using data captured on a real-world Android device and the MalGenome dataset demonstrate the effectiveness of our approach, in comparison to two hybrid optimization methods which are differential evolution (ANFIS-DE) and ant colony optimization (ANFIS-ACO).

DyHAP: Dynamic Hybrid ANFIS-PSO Approach for Predicting Mobile Malware

PubMed Central

Afifi, Firdaus; Anuar, Nor Badrul; Shamshirband, Shahaboddin

2016-01-01

To deal with the large number of malicious mobile applications (e.g. mobile malware), a number of malware detection systems have been proposed in the literature. In this paper, we propose a hybrid method to find the optimum parameters that can be used to facilitate mobile malware identification. We also present a multi agent system architecture comprising three system agents (i.e. sniffer, extraction and selection agent) to capture and manage the pcap file for data preparation phase. In our hybrid approach, we combine an adaptive neuro fuzzy inference system (ANFIS) and particle swarm optimization (PSO). Evaluations using data captured on a real-world Android device and the MalGenome dataset demonstrate the effectiveness of our approach, in comparison to two hybrid optimization methods which are differential evolution (ANFIS-DE) and ant colony optimization (ANFIS-ACO). PMID:27611312

The approach for shortest paths in fire succor based on component GIS technology

NASA Astrophysics Data System (ADS)

Han, Jie; Zhao, Yong; Dai, K. W.

2007-06-01

Fire safety is an important issue for the national economy and people's living. Efficiency and exactness of fire department succor directly relate to safety of peoples' lives and property. Many disadvantages of the traditional fire system have been emerged in practical applications. The preparation of pumpers is guided by wireless communication or wire communication, so its real-time and accurate performances are much poorer. The information about the reported fire, such as the position, disaster and map, et al., for alarm and command was processed by persons, which slows the reaction speed and delays the combat opportunity. In order to solve these disadvantages, it has an important role to construct a modern fire command center based on high technology. The construction of modern fire command center can realize the modernization and automation of fire command and management. It will play a great role in protecting safety of peoples' lives and property. The center can enhance battle ability and can reduce the direct and indirect loss of fire damage at most. With the development of science technology, Geographic Information System (GIS) has becoming a new information industry for hardware production, software development, data collection, space analysis and counseling. With the popularization of computers and the development of GIS, GIS has gained increasing broad applications for its strong functionality. Network analysis is one of the most important functions of GIS, and the most elementary and pivotal issue of network analysis is the calculation of shortest paths. The shortest paths are mostly applied to some emergent systems such as 119 fire alarms. These systems mainly require that the computation time of the optimal path should be 1-3 seconds. And during traveling, the next running path of the vehicles should be calculated in time. So the implement of the shortest paths must have a high efficiency. In this paper, the component GIS technology was applied to collect and record the data information (such as, the situation of this disaster, map and road status et al) of the reported fire firstly. The ant colony optimization was used to calculate the shortest path of fire succor secondly. The optimization results were sent to the pumpers, which can let pumpers choose the shortest paths intelligently and come to fire position with least time. The programming method for shortest paths is proposed in section 3. There are three parts in this section. The elementary framework of the proposed programming method is presented in part one. The systematic framework of GIS component is described in part two. The ant colony optimization employed is presented in part three. In section 4, a simple application instance was presented to demonstrate the proposed programming method. There are three parts in this section. The distributed Web application based on component GIS was described in part one. The optimization results without traffic constraint were presented in part two. The optimization results with traffic constraint were presented in part three. The contributions of this paper can be summarized as follows. (1) It proposed an effective approach for shortest paths in fire succor based on component GIS technology. This proposed approach can achieve the real-time decisions of shortest paths for fire succor. (2) It applied the ant colony optimization to implement the shortest path decision. The traffic information was considered in the shortest path decision using ant colony optimization. The final application instance suggests that the proposed approach is feasible, correct and valid.

Improved ant colony optimization for optimal crop and irrigation water allocation by incorporating domain knowledge

USDA-ARS?s Scientific Manuscript database

An improved ant colony optimization (ACO) formulation for the allocation of crops and water to different irrigation areas is developed. The formulation enables dynamic adjustment of decision variable options and makes use of visibility factors (VFs, the domain knowledge that can be used to identify ...

Spatial optimization of prairie dog colonies for black-footed ferret recovery

Treesearch

Michael Bevers; John G. Hof; Daniel W. Uresk; Gregory L. Schenbeck

1997-01-01

A discrete-time reaction-diffusion model for black-footed ferret release, population growth, and dispersal is combined with ferret carrying capacity constraints based on prairie dog population management decisions to form a spatial optimization model. Spatial arrangement of active prairie dog colonies within a ferret reintroduction area is optimized over time for...

Optimization of Stereo Matching in 3D Reconstruction Based on Binocular Vision

NASA Astrophysics Data System (ADS)

Gai, Qiyang

2018-01-01

Stereo matching is one of the key steps of 3D reconstruction based on binocular vision. In order to improve the convergence speed and accuracy in 3D reconstruction based on binocular vision, this paper adopts the combination method of polar constraint and ant colony algorithm. By using the line constraint to reduce the search range, an ant colony algorithm is used to optimize the stereo matching feature search function in the proposed search range. Through the establishment of the stereo matching optimization process analysis model of ant colony algorithm, the global optimization solution of stereo matching in 3D reconstruction based on binocular vision system is realized. The simulation results show that by the combining the advantage of polar constraint and ant colony algorithm, the stereo matching range of 3D reconstruction based on binocular vision is simplified, and the convergence speed and accuracy of this stereo matching process are improved.

Interplay between intrinsic noise and the stochasticity of the cell cycle in bacterial colonies.

PubMed

Canela-Xandri, Oriol; Sagués, Francesc; Buceta, Javier

2010-06-02

Herein we report on the effects that different stochastic contributions induce in bacterial colonies in terms of protein concentration and production. In particular, we consider for what we believe to be the first time cell-to-cell diversity due to the unavoidable randomness of the cell-cycle duration and its interplay with other noise sources. To that end, we model a recent experimental setup that implements a protein dilution protocol by means of division events to characterize the gene regulatory function at the single cell level. This approach allows us to investigate the effect of different stochastic terms upon the total randomness experimentally reported for the gene regulatory function. In addition, we show that the interplay between intrinsic fluctuations and the stochasticity of the cell-cycle duration leads to different constructive roles. On the one hand, we show that there is an optimal value of protein concentration (alternatively an optimal value of the cell cycle phase) such that the noise in protein concentration attains a minimum. On the other hand, we reveal that there is an optimal value of the stochasticity of the cell cycle duration such that the coherence of the protein production with respect to the colony average production is maximized. The latter can be considered as a novel example of the recently reported phenomenon of diversity induced resonance. Copyright (c) 2010 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Ant Colony Optimization Analysis on Overall Stability of High Arch Dam Basis of Field Monitoring

PubMed Central

Liu, Xiaoli; Chen, Hong-Xin; Kim, Jinxie

2014-01-01

A dam ant colony optimization (D-ACO) analysis of the overall stability of high arch dams on complicated foundations is presented in this paper. A modified ant colony optimization (ACO) model is proposed for obtaining dam concrete and rock mechanical parameters. A typical dam parameter feedback problem is proposed for nonlinear back-analysis numerical model based on field monitoring deformation and ACO. The basic principle of the proposed model is the establishment of the objective function of optimizing real concrete and rock mechanical parameter. The feedback analysis is then implemented with a modified ant colony algorithm. The algorithm performance is satisfactory, and the accuracy is verified. The m groups of feedback parameters, used to run a nonlinear FEM code, and the displacement and stress distribution are discussed. A feedback analysis of the deformation of the Lijiaxia arch dam and based on the modified ant colony optimization method is also conducted. By considering various material parameters obtained using different analysis methods, comparative analyses were conducted on dam displacements, stress distribution characteristics, and overall dam stability. The comparison results show that the proposal model can effectively solve for feedback multiple parameters of dam concrete and rock material and basically satisfy assessment requirements for geotechnical structural engineering discipline. PMID:25025089

Ant colony optimization for solving university facility layout problem

NASA Astrophysics Data System (ADS)

Mohd Jani, Nurul Hafiza; Mohd Radzi, Nor Haizan; Ngadiman, Mohd Salihin

2013-04-01

Quadratic Assignment Problems (QAP) is classified as the NP hard problem. It has been used to model a lot of problem in several areas such as operational research, combinatorial data analysis and also parallel and distributed computing, optimization problem such as graph portioning and Travel Salesman Problem (TSP). In the literature, researcher use exact algorithm, heuristics algorithm and metaheuristic approaches to solve QAP problem. QAP is largely applied in facility layout problem (FLP). In this paper we used QAP to model university facility layout problem. There are 8 facilities that need to be assigned to 8 locations. Hence we have modeled a QAP problem with n ≤ 10 and developed an Ant Colony Optimization (ACO) algorithm to solve the university facility layout problem. The objective is to assign n facilities to n locations such that the minimum product of flows and distances is obtained. Flow is the movement from one to another facility, whereas distance is the distance between one locations of a facility to other facilities locations. The objective of the QAP is to obtain minimum total walking (flow) of lecturers from one destination to another (distance).

Coordination Between the Sexes Constrains the Optimization of Reproductive Timing in Honey Bee Colonies.

PubMed

Lemanski, Natalie J; Fefferman, Nina H

2017-06-01

Honeybees are an excellent model system for examining how trade-offs shape reproductive timing in organisms with seasonal environments. Honeybee colonies reproduce two ways: producing swarms comprising a queen and thousands of workers or producing males (drones). There is an energetic trade-off between producing workers, which contribute to colony growth, and drones, which contribute only to reproduction. The timing of drone production therefore determines both the drones' likelihood of mating and when colonies reach sufficient size to swarm. Using a linear programming model, we ask when a colony should produce drones and swarms to maximize reproductive success. We find the optimal behavior for each colony is to produce all drones prior to swarming, an impossible solution on a population scale because queens and drones would never co-occur. Reproductive timing is therefore not solely determined by energetic trade-offs but by the game theoretic problem of coordinating the production of reproductives among colonies.

Allometry of individual reproduction and defense in eusocial colonies: A comparative approach to trade-offs in social sponge-dwelling Synalpheus shrimps.

PubMed

Bornbusch, Sarah L; Lefcheck, Jonathan S; Duffy, J Emmett

2018-01-01

Eusociality, one of the most complex forms of social organization, is thought to have evolved in several animal clades in response to competition for resources and reproductive opportunities. Several species of snapping shrimp in the genus Synalpheus, the only marine organisms known to exhibit eusociality, form colonies characterized by high reproductive skew, and aggressive territoriality coupled with cooperative defense. In eusocial Synalpheus colonies, individual reproduction is limited to female 'queens', whose fecundity dictates colony growth. Given that individual reproduction and defense are both energetically costly, individual and colony fitness likely depend on the optimal allocation of resources by these reproducing individuals towards these potentially competing demands. Synalpheus species, however, display varying degrees of eusociality, suggesting that reproducing females have adopted different strategies for allocation among reproduction and defense. Here, we use structural equation modeling to characterize the relationships between the allometry of queen reproductive capacity and defensive weaponry, and colony size in six eusocial Synalpheus species, estimating trade-offs between reproduction and defense. We document strong trade-offs between mass of the fighting claw (defense) and egg number (reproduction) in queens from weakly eusocial species, while the trade-off is reduced or absent in those from strongly eusocial species. These results suggest that in less cooperative species, intra-colony conflict selects for queen retention of weapons that have significant costs to fecundity, while reproducing females from highly eusocial species, i.e., those with a single queen, have been able to reduce the cost of weapons as a result of protection by other colony members.

Ant colony system algorithm for the optimization of beer fermentation control.

PubMed

Xiao, Jie; Zhou, Ze-Kui; Zhang, Guang-Xin

2004-12-01

Beer fermentation is a dynamic process that must be guided along a temperature profile to obtain the desired results. Ant colony system algorithm was applied to optimize the kinetic model of this process. During a fixed period of fermentation time, a series of different temperature profiles of the mixture were constructed. An optimal one was chosen at last. Optimal temperature profile maximized the final ethanol production and minimized the byproducts concentration and spoilage risk. The satisfactory results obtained did not require much computation effort.

Ant-cuckoo colony optimization for feature selection in digital mammogram.

PubMed

Jona, J B; Nagaveni, N

2014-01-15

Digital mammogram is the only effective screening method to detect the breast cancer. Gray Level Co-occurrence Matrix (GLCM) textural features are extracted from the mammogram. All the features are not essential to detect the mammogram. Therefore identifying the relevant feature is the aim of this work. Feature selection improves the classification rate and accuracy of any classifier. In this study, a new hybrid metaheuristic named Ant-Cuckoo Colony Optimization a hybrid of Ant Colony Optimization (ACO) and Cuckoo Search (CS) is proposed for feature selection in Digital Mammogram. ACO is a good metaheuristic optimization technique but the drawback of this algorithm is that the ant will walk through the path where the pheromone density is high which makes the whole process slow hence CS is employed to carry out the local search of ACO. Support Vector Machine (SVM) classifier with Radial Basis Kernal Function (RBF) is done along with the ACO to classify the normal mammogram from the abnormal mammogram. Experiments are conducted in miniMIAS database. The performance of the new hybrid algorithm is compared with the ACO and PSO algorithm. The results show that the hybrid Ant-Cuckoo Colony Optimization algorithm is more accurate than the other techniques.

Multi-AUV autonomous task planning based on the scroll time domain quantum bee colony optimization algorithm in uncertain environment

PubMed Central

Zhang, Rubo; Yang, Yu

2017-01-01

Research on distributed task planning model for multi-autonomous underwater vehicle (MAUV). A scroll time domain quantum artificial bee colony (STDQABC) optimization algorithm is proposed to solve the multi-AUV optimal task planning scheme. In the uncertain marine environment, the rolling time domain control technique is used to realize a numerical optimization in a narrowed time range. Rolling time domain control is one of the better task planning techniques, which can greatly reduce the computational workload and realize the tradeoff between AUV dynamics, environment and cost. Finally, a simulation experiment was performed to evaluate the distributed task planning performance of the scroll time domain quantum bee colony optimization algorithm. The simulation results demonstrate that the STDQABC algorithm converges faster than the QABC and ABC algorithms in terms of both iterations and running time. The STDQABC algorithm can effectively improve MAUV distributed tasking planning performance, complete the task goal and get the approximate optimal solution. PMID:29186166

Multi-AUV autonomous task planning based on the scroll time domain quantum bee colony optimization algorithm in uncertain environment.

PubMed

Li, Jianjun; Zhang, Rubo; Yang, Yu

2017-01-01

Research on distributed task planning model for multi-autonomous underwater vehicle (MAUV). A scroll time domain quantum artificial bee colony (STDQABC) optimization algorithm is proposed to solve the multi-AUV optimal task planning scheme. In the uncertain marine environment, the rolling time domain control technique is used to realize a numerical optimization in a narrowed time range. Rolling time domain control is one of the better task planning techniques, which can greatly reduce the computational workload and realize the tradeoff between AUV dynamics, environment and cost. Finally, a simulation experiment was performed to evaluate the distributed task planning performance of the scroll time domain quantum bee colony optimization algorithm. The simulation results demonstrate that the STDQABC algorithm converges faster than the QABC and ABC algorithms in terms of both iterations and running time. The STDQABC algorithm can effectively improve MAUV distributed tasking planning performance, complete the task goal and get the approximate optimal solution.

A novel comprehensive learning artificial bee colony optimizer for dynamic optimization biological problems.

PubMed

Su, Weixing; Chen, Hanning; Liu, Fang; Lin, Na; Jing, Shikai; Liang, Xiaodan; Liu, Wei

2017-03-01

There are many dynamic optimization problems in the real world, whose convergence and searching ability is cautiously desired, obviously different from static optimization cases. This requires an optimization algorithm adaptively seek the changing optima over dynamic environments, instead of only finding the global optimal solution in the static environment. This paper proposes a novel comprehensive learning artificial bee colony optimizer (CLABC) for optimization in dynamic environments problems, which employs a pool of optimal foraging strategies to balance the exploration and exploitation tradeoff. The main motive of CLABC is to enrich artificial bee foraging behaviors in the ABC model by combining Powell's pattern search method, life-cycle, and crossover-based social learning strategy. The proposed CLABC is a more bee-colony-realistic model that the bee can reproduce and die dynamically throughout the foraging process and population size varies as the algorithm runs. The experiments for evaluating CLABC are conducted on the dynamic moving peak benchmarks. Furthermore, the proposed algorithm is applied to a real-world application of dynamic RFID network optimization. Statistical analysis of all these cases highlights the significant performance improvement due to the beneficial combination and demonstrates the performance superiority of the proposed algorithm.

A seismic fault recognition method based on ant colony optimization

NASA Astrophysics Data System (ADS)

Chen, Lei; Xiao, Chuangbai; Li, Xueliang; Wang, Zhenli; Huo, Shoudong

2018-05-01

Fault recognition is an important section in seismic interpretation and there are many methods for this technology, but no one can recognize fault exactly enough. For this problem, we proposed a new fault recognition method based on ant colony optimization which can locate fault precisely and extract fault from the seismic section. Firstly, seismic horizons are extracted by the connected component labeling algorithm; secondly, the fault location are decided according to the horizontal endpoints of each horizon; thirdly, the whole seismic section is divided into several rectangular blocks and the top and bottom endpoints of each rectangular block are considered as the nest and food respectively for the ant colony optimization algorithm. Besides that, the positive section is taken as an actual three dimensional terrain by using the seismic amplitude as a height. After that, the optimal route from nest to food calculated by the ant colony in each block is judged as a fault. Finally, extensive comparative tests were performed on the real seismic data. Availability and advancement of the proposed method were validated by the experimental results.

ACOustic: A Nature-Inspired Exploration Indicator for Ant Colony Optimization.

PubMed

Sagban, Rafid; Ku-Mahamud, Ku Ruhana; Abu Bakar, Muhamad Shahbani

2015-01-01

A statistical machine learning indicator, ACOustic, is proposed to evaluate the exploration behavior in the iterations of ant colony optimization algorithms. This idea is inspired by the behavior of some parasites in their mimicry to the queens' acoustics of their ant hosts. The parasites' reaction results from their ability to indicate the state of penetration. The proposed indicator solves the problem of robustness that results from the difference of magnitudes in the distance's matrix, especially when combinatorial optimization problems with rugged fitness landscape are applied. The performance of the proposed indicator is evaluated against the existing indicators in six variants of ant colony optimization algorithms. Instances for travelling salesman problem and quadratic assignment problem are used in the experimental evaluation. The analytical results showed that the proposed indicator is more informative and more robust.

Fuzzy Sets in Dynamic Adaptation of Parameters of a Bee Colony Optimization for Controlling the Trajectory of an Autonomous Mobile Robot

PubMed Central

Amador-Angulo, Leticia; Mendoza, Olivia; Castro, Juan R.; Rodríguez-Díaz, Antonio; Melin, Patricia; Castillo, Oscar

2016-01-01

A hybrid approach composed by different types of fuzzy systems, such as the Type-1 Fuzzy Logic System (T1FLS), Interval Type-2 Fuzzy Logic System (IT2FLS) and Generalized Type-2 Fuzzy Logic System (GT2FLS) for the dynamic adaptation of the alpha and beta parameters of a Bee Colony Optimization (BCO) algorithm is presented. The objective of the work is to focus on the BCO technique to find the optimal distribution of the membership functions in the design of fuzzy controllers. We use BCO specifically for tuning membership functions of the fuzzy controller for trajectory stability in an autonomous mobile robot. We add two types of perturbations in the model for the Generalized Type-2 Fuzzy Logic System to better analyze its behavior under uncertainty and this shows better results when compared to the original BCO. We implemented various performance indices; ITAE, IAE, ISE, ITSE, RMSE and MSE to measure the performance of the controller. The experimental results show better performances using GT2FLS then by IT2FLS and T1FLS in the dynamic adaptation the parameters for the BCO algorithm. PMID:27618062

A quality-by-design approach to risk reduction and optimization for human embryonic stem cell cryopreservation processes.

PubMed

Mitchell, Peter D; Ratcliffe, Elizabeth; Hourd, Paul; Williams, David J; Thomas, Robert J

2014-12-01

It is well documented that cryopreservation and resuscitation of human embryonic stem cells (hESCs) is complex and ill-defined, and often suffers poor cell recovery and increased levels of undesirable cell differentiation. In this study we have applied Quality-by-Design (QbD) concepts to the critical processes of slow-freeze cryopreservation and resuscitation of hESC colony cultures. Optimized subprocesses were linked together to deliver a controlled complete process. We have demonstrated a rapid, high-throughput, and stable system for measurement of cell adherence and viability as robust markers of in-process and postrecovery cell state. We observed that measurement of adherence and viability of adhered cells at 1 h postseeding was predictive of cell proliferative ability up to 96 h in this system. Application of factorial design defined the operating spaces for cryopreservation and resuscitation, critically linking the performance of these two processes. Optimization of both processes resulted in enhanced reattachment and post-thaw viability, resulting in substantially greater recovery of cryopreserved, pluripotent cell colonies. This study demonstrates the importance of QbD concepts and tools for rapid, robust, and low-risk process design that can inform manufacturing controls and logistics.

Dynamic optimization of chemical processes using ant colony framework.

PubMed

Rajesh, J; Gupta, K; Kusumakar, H S; Jayaraman, V K; Kulkarni, B D

2001-11-01

Ant colony framework is illustrated by considering dynamic optimization of six important bench marking examples. This new computational tool is simple to implement and can tackle problems with state as well as terminal constraints in a straightforward fashion. It requires fewer grid points to reach the global optimum at relatively very low computational effort. The examples with varying degree of complexities, analyzed here, illustrate its potential for solving a large class of process optimization problems in chemical engineering.

Directed Bee Colony Optimization Algorithm to Solve the Nurse Rostering Problem.

PubMed

Rajeswari, M; Amudhavel, J; Pothula, Sujatha; Dhavachelvan, P

2017-01-01

The Nurse Rostering Problem is an NP-hard combinatorial optimization, scheduling problem for assigning a set of nurses to shifts per day by considering both hard and soft constraints. A novel metaheuristic technique is required for solving Nurse Rostering Problem (NRP). This work proposes a metaheuristic technique called Directed Bee Colony Optimization Algorithm using the Modified Nelder-Mead Method for solving the NRP. To solve the NRP, the authors used a multiobjective mathematical programming model and proposed a methodology for the adaptation of a Multiobjective Directed Bee Colony Optimization (MODBCO). MODBCO is used successfully for solving the multiobjective problem of optimizing the scheduling problems. This MODBCO is an integration of deterministic local search, multiagent particle system environment, and honey bee decision-making process. The performance of the algorithm is assessed using the standard dataset INRC2010, and it reflects many real-world cases which vary in size and complexity. The experimental analysis uses statistical tools to show the uniqueness of the algorithm on assessment criteria.

Skull removal in MR images using a modified artificial bee colony optimization algorithm.

PubMed

Taherdangkoo, Mohammad

2014-01-01

Removal of the skull from brain Magnetic Resonance (MR) images is an important preprocessing step required for other image analysis techniques such as brain tissue segmentation. In this paper, we propose a new algorithm based on the Artificial Bee Colony (ABC) optimization algorithm to remove the skull region from brain MR images. We modify the ABC algorithm using a different strategy for initializing the coordinates of scout bees and their direction of search. Moreover, we impose an additional constraint to the ABC algorithm to avoid the creation of discontinuous regions. We found that our algorithm successfully removed all bony skull from a sample of de-identified MR brain images acquired from different model scanners. The obtained results of the proposed algorithm compared with those of previously introduced well known optimization algorithms such as Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) demonstrate the superior results and computational performance of our algorithm, suggesting its potential for clinical applications.

Directed Bee Colony Optimization Algorithm to Solve the Nurse Rostering Problem

PubMed Central

Amudhavel, J.; Pothula, Sujatha; Dhavachelvan, P.

2017-01-01

The Nurse Rostering Problem is an NP-hard combinatorial optimization, scheduling problem for assigning a set of nurses to shifts per day by considering both hard and soft constraints. A novel metaheuristic technique is required for solving Nurse Rostering Problem (NRP). This work proposes a metaheuristic technique called Directed Bee Colony Optimization Algorithm using the Modified Nelder-Mead Method for solving the NRP. To solve the NRP, the authors used a multiobjective mathematical programming model and proposed a methodology for the adaptation of a Multiobjective Directed Bee Colony Optimization (MODBCO). MODBCO is used successfully for solving the multiobjective problem of optimizing the scheduling problems. This MODBCO is an integration of deterministic local search, multiagent particle system environment, and honey bee decision-making process. The performance of the algorithm is assessed using the standard dataset INRC2010, and it reflects many real-world cases which vary in size and complexity. The experimental analysis uses statistical tools to show the uniqueness of the algorithm on assessment criteria. PMID:28473849

An improved self-adaptive ant colony algorithm based on genetic strategy for the traveling salesman problem

NASA Astrophysics Data System (ADS)

Wang, Pan; Zhang, Yi; Yan, Dong

2018-05-01

Ant Colony Algorithm (ACA) is a powerful and effective algorithm for solving the combination optimization problem. Moreover, it was successfully used in traveling salesman problem (TSP). But it is easy to prematurely converge to the non-global optimal solution and the calculation time is too long. To overcome those shortcomings, a new method is presented-An improved self-adaptive Ant Colony Algorithm based on genetic strategy. The proposed method adopts adaptive strategy to adjust the parameters dynamically. And new crossover operation and inversion operation in genetic strategy was used in this method. We also make an experiment using the well-known data in TSPLIB. The experiment results show that the performance of the proposed method is better than the basic Ant Colony Algorithm and some improved ACA in both the result and the convergence time. The numerical results obtained also show that the proposed optimization method can achieve results close to the theoretical best known solutions at present.

Intercellular Genomics of Subsurface Microbial Colonies

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ortoleva, Peter; Tuncay, Kagan; Gannon, Dennis

2007-02-14

This report summarizes progress in the second year of this project. The objective is to develop methods and software to predict the spatial configuration, properties and temporal evolution of microbial colonies in the subsurface. To accomplish this, we integrate models of intracellular processes, cell-host medium exchange and reaction-transport dynamics on the colony scale. At the conclusion of the project, we aim to have the foundations of a predictive mathematical model and software that captures the three scales of these systems – the intracellular, pore, and colony wide spatial scales. In the second year of the project, we refined our transcriptionalmore » regulatory network discovery (TRND) approach that utilizes gene expression data along with phylogenic similarity and gene ontology analyses and applied it successfully to E.coli, human B cells, and Geobacter sulfurreducens. We have developed a new Web interface, GeoGen, which is tailored to the reconstruction of microbial TRNs and solely focuses on Geobacter as one of DOE’s high priority microbes. Our developments are designed such that the frameworks for the TRND and GeoGen can readily be used for other microbes of interest to the DOE. In the context of modeling a single bacterium, we are actively pursuing both steady-state and kinetic approaches. The steady-state approach is based on a flux balance that uses maximizing biomass growth rate as its objective, subjected to various biochemical constraints, for the optimal values of reaction rates and uptake/release of metabolites. For the kinetic approach, we use Karyote, a rigorous cell model developed by us for an earlier DOE grant and the DARPA BioSPICE Project. We are also investigating the interplay between bacterial colonies and environment at both pore and macroscopic scales. The pore scale models use detailed representations for realistic porous media accounting for the distribution of grain size whereas the macroscopic models employ the Darcy-type flow equations and up-scaled advective-diffusive transport equations for chemical species. We are rigorously testing the relationship between these two scales by evaluating macroscopic parameters using the volume averaging methodology applied to pore scale model results.« less

Optimization of Straight Cylindrical Turning Using Artificial Bee Colony (ABC) Algorithm

NASA Astrophysics Data System (ADS)

Prasanth, Rajanampalli Seshasai Srinivasa; Hans Raj, Kandikonda

2017-04-01

Artificial bee colony (ABC) algorithm, that mimics the intelligent foraging behavior of honey bees, is increasingly gaining acceptance in the field of process optimization, as it is capable of handling nonlinearity, complexity and uncertainty. Straight cylindrical turning is a complex and nonlinear machining process which involves the selection of appropriate cutting parameters that affect the quality of the workpiece. This paper presents the estimation of optimal cutting parameters of the straight cylindrical turning process using the ABC algorithm. The ABC algorithm is first tested on four benchmark problems of numerical optimization and its performance is compared with genetic algorithm (GA) and ant colony optimization (ACO) algorithm. Results indicate that, the rate of convergence of ABC algorithm is better than GA and ACO. Then, the ABC algorithm is used to predict optimal cutting parameters such as cutting speed, feed rate, depth of cut and tool nose radius to achieve good surface finish. Results indicate that, the ABC algorithm estimated a comparable surface finish when compared with real coded genetic algorithm and differential evolution algorithm.

Artificial bee colony algorithm for constrained possibilistic portfolio optimization problem

NASA Astrophysics Data System (ADS)

Chen, Wei

2015-07-01

In this paper, we discuss the portfolio optimization problem with real-world constraints under the assumption that the returns of risky assets are fuzzy numbers. A new possibilistic mean-semiabsolute deviation model is proposed, in which transaction costs, cardinality and quantity constraints are considered. Due to such constraints the proposed model becomes a mixed integer nonlinear programming problem and traditional optimization methods fail to find the optimal solution efficiently. Thus, a modified artificial bee colony (MABC) algorithm is developed to solve the corresponding optimization problem. Finally, a numerical example is given to illustrate the effectiveness of the proposed model and the corresponding algorithm.

[Application of rational ant colony optimization to improve the reproducibility degree of laser three-dimensional copy].

PubMed

Cui, Xiao-Yan; Huo, Zhong-Gang; Xin, Zhong-Hua; Tian, Xiao; Zhang, Xiao-Dong

2013-07-01

Three-dimensional (3D) copying of artificial ears and pistol printing are pushing laser three-dimensional copying technique to a new page. Laser three-dimensional scanning is a fresh field in laser application, and plays an irreplaceable part in three-dimensional copying. Its accuracy is the highest among all present copying techniques. Reproducibility degree marks the agreement of copied object with the original object on geometry, being the most important index property in laser three-dimensional copying technique. In the present paper, the error of laser three-dimensional copying was analyzed. The conclusion is that the data processing to the point cloud of laser scanning is the key technique to reduce the error and increase the reproducibility degree. The main innovation of this paper is as follows. On the basis of traditional ant colony optimization, rational ant colony optimization algorithm proposed by the author was applied to the laser three-dimensional copying as a new algorithm, and was put into practice. Compared with customary algorithm, rational ant colony optimization algorithm shows distinct advantages in data processing of laser three-dimensional copying, reducing the error and increasing the reproducibility degree of the copy.

Hyperspectral imaging for presumptive identification of bacterial colonies on solid chromogenic culture media

NASA Astrophysics Data System (ADS)

Guillemot, Mathilde; Midahuen, Rony; Archeny, Delpine; Fulchiron, Corine; Montvernay, Regis; Perrin, Guillaume; Leroux, Denis F.

2016-04-01

BioMérieux is automating the microbiology laboratory in order to reduce cost (less manpower and consumables), to improve performance (increased sensitivity, machine algorithms) and to gain traceability through optimization of the clinical laboratory workflow. In this study, we evaluate the potential of Hyperspectral imaging (HSI) as a substitute to human visual observation when performing the task of microbiological culture interpretation. Microbial colonies from 19 strains subcategorized in 6 chromogenic classes were analyzed after a 24h-growth on a chromogenic culture medium (chromID® CPS Elite, bioMérieux, France). The HSI analysis was performed in the VNIR region (400-900 nm) using a linescan configuration. Using algorithms relying on Linear Spectral Unmixing, and using exclusively Diffuse Reflectance Spectra (DRS) as input data, we report interclass classification accuracies of 100% using a fully automatable approach and no use of morphological information. In order to eventually simplify the instrument, the performance of degraded DRS was also evaluated using only the most discriminant 14 spectral channels (a model for a multispectral approach) or 3 channels (model of a RGB image). The overall classification performance remains unchanged for our multispectral model but is degraded for the predicted RGB model, hints that a multispectral solution might bring the answer for an improved colony recognition.

A preliminary study to metaheuristic approach in multilayer radiation shielding optimization

NASA Astrophysics Data System (ADS)

Arif Sazali, Muhammad; Rashid, Nahrul Khair Alang Md; Hamzah, Khaidzir

2018-01-01

Metaheuristics are high-level algorithmic concepts that can be used to develop heuristic optimization algorithms. One of their applications is to find optimal or near optimal solutions to combinatorial optimization problems (COPs) such as scheduling, vehicle routing, and timetabling. Combinatorial optimization deals with finding optimal combinations or permutations in a given set of problem components when exhaustive search is not feasible. A radiation shield made of several layers of different materials can be regarded as a COP. The time taken to optimize the shield may be too high when several parameters are involved such as the number of materials, the thickness of layers, and the arrangement of materials. Metaheuristics can be applied to reduce the optimization time, trading guaranteed optimal solutions for near-optimal solutions in comparably short amount of time. The application of metaheuristics for radiation shield optimization is lacking. In this paper, we present a review on the suitability of using metaheuristics in multilayer shielding design, specifically the genetic algorithm and ant colony optimization algorithm (ACO). We would also like to propose an optimization model based on the ACO method.

Modeling design iteration in product design and development and its solution by a novel artificial bee colony algorithm.

PubMed

Chen, Tinggui; Xiao, Renbin

2014-01-01

Due to fierce market competition, how to improve product quality and reduce development cost determines the core competitiveness of enterprises. However, design iteration generally causes increases of product cost and delays of development time as well, so how to identify and model couplings among tasks in product design and development has become an important issue for enterprises to settle. In this paper, the shortcomings existing in WTM model are discussed and tearing approach as well as inner iteration method is used to complement the classic WTM model. In addition, the ABC algorithm is also introduced to find out the optimal decoupling schemes. In this paper, firstly, tearing approach and inner iteration method are analyzed for solving coupled sets. Secondly, a hybrid iteration model combining these two technologies is set up. Thirdly, a high-performance swarm intelligence algorithm, artificial bee colony, is adopted to realize problem-solving. Finally, an engineering design of a chemical processing system is given in order to verify its reasonability and effectiveness.

The research of autonomous obstacle avoidance of mobile robot based on multi-sensor integration

NASA Astrophysics Data System (ADS)

Zhao, Ming; Han, Baoling

2016-11-01

The object of this study is the bionic quadruped mobile robot. The study has proposed a system design plan for mobile robot obstacle avoidance with the binocular stereo visual sensor and the self-control 3D Lidar integrated with modified ant colony optimization path planning to realize the reconstruction of the environmental map. Because the working condition of a mobile robot is complex, the result of the 3D reconstruction with a single binocular sensor is undesirable when feature points are few and the light condition is poor. Therefore, this system integrates the stereo vision sensor blumblebee2 and the Lidar sensor together to detect the cloud information of 3D points of environmental obstacles. This paper proposes the sensor information fusion technology to rebuild the environment map. Firstly, according to the Lidar data and visual data on obstacle detection respectively, and then consider two methods respectively to detect the distribution of obstacles. Finally fusing the data to get the more complete, more accurate distribution of obstacles in the scene. Then the thesis introduces ant colony algorithm. It has analyzed advantages and disadvantages of the ant colony optimization and its formation cause deeply, and then improved the system with the help of the ant colony optimization to increase the rate of convergence and precision of the algorithm in robot path planning. Such improvements and integrations overcome the shortcomings of the ant colony optimization like involving into the local optimal solution easily, slow search speed and poor search results. This experiment deals with images and programs the motor drive under the compiling environment of Matlab and Visual Studio and establishes the visual 2.5D grid map. Finally it plans a global path for the mobile robot according to the ant colony algorithm. The feasibility and effectiveness of the system are confirmed by ROS and simulation platform of Linux.

Item Selection for the Development of Short Forms of Scales Using an Ant Colony Optimization Algorithm

ERIC Educational Resources Information Center

Leite, Walter L.; Huang, I-Chan; Marcoulides, George A.

2008-01-01

This article presents the use of an ant colony optimization (ACO) algorithm for the development of short forms of scales. An example 22-item short form is developed for the Diabetes-39 scale, a quality-of-life scale for diabetes patients, using a sample of 265 diabetes patients. A simulation study comparing the performance of the ACO algorithm and…

Developing a reading concentration monitoring system by applying an artificial bee colony algorithm to e-books in an intelligent classroom.

PubMed

Hsu, Chia-Cheng; Chen, Hsin-Chin; Su, Yen-Ning; Huang, Kuo-Kuang; Huang, Yueh-Min

2012-10-22

A growing number of educational studies apply sensors to improve student learning in real classroom settings. However, how can sensors be integrated into classrooms to help instructors find out students' reading concentration rates and thus better increase learning effectiveness? The aim of the current study was to develop a reading concentration monitoring system for use with e-books in an intelligent classroom and to help instructors find out the students' reading concentration rates. The proposed system uses three types of sensor technologies, namely a webcam, heartbeat sensor, and blood oxygen sensor to detect the learning behaviors of students by capturing various physiological signals. An artificial bee colony (ABC) optimization approach is applied to the data gathered from these sensors to help instructors understand their students' reading concentration rates in a classroom learning environment. The results show that the use of the ABC algorithm in the proposed system can effectively obtain near-optimal solutions. The system has a user-friendly graphical interface, making it easy for instructors to clearly understand the reading status of their students.

A multipath routing protocol based on clustering and ant colony optimization for wireless sensor networks.

PubMed

Yang, Jing; Xu, Mai; Zhao, Wei; Xu, Baoguo

2010-01-01

For monitoring burst events in a kind of reactive wireless sensor networks (WSNs), a multipath routing protocol (MRP) based on dynamic clustering and ant colony optimization (ACO) is proposed. Such an approach can maximize the network lifetime and reduce the energy consumption. An important attribute of WSNs is their limited power supply, and therefore some metrics (such as energy consumption of communication among nodes, residual energy, path length) were considered as very important criteria while designing routing in the MRP. Firstly, a cluster head (CH) is selected among nodes located in the event area according to some parameters, such as residual energy. Secondly, an improved ACO algorithm is applied in the search for multiple paths between the CH and sink node. Finally, the CH dynamically chooses a route to transmit data with a probability that depends on many path metrics, such as energy consumption. The simulation results show that MRP can prolong the network lifetime, as well as balance of energy consumption among nodes and reduce the average energy consumption effectively.

Enhanced ant colony optimization for inventory routing problem

NASA Astrophysics Data System (ADS)

Wong, Lily; Moin, Noor Hasnah

2015-10-01

The inventory routing problem (IRP) integrates and coordinates two important components of supply chain management which are transportation and inventory management. We consider a one-to-many IRP network for a finite planning horizon. The demand for each product is deterministic and time varying as well as a fleet of capacitated homogeneous vehicles, housed at a depot/warehouse, delivers the products from the warehouse to meet the demand specified by the customers in each period. The inventory holding cost is product specific and is incurred at the customer sites. The objective is to determine the amount of inventory and to construct a delivery routing that minimizes both the total transportation and inventory holding cost while ensuring each customer's demand is met over the planning horizon. The problem is formulated as a mixed integer programming problem and is solved using CPLEX 12.4 to get the lower and upper bound (best integer) for each instance considered. We propose an enhanced ant colony optimization (ACO) to solve the problem and the built route is improved by using local search. The computational experiments demonstrating the effectiveness of our approach is presented.

Developing a Reading Concentration Monitoring System by Applying an Artificial Bee Colony Algorithm to E-Books in an Intelligent Classroom

PubMed Central

Hsu, Chia-Cheng; Chen, Hsin-Chin; Su, Yen-Ning; Huang, Kuo-Kuang; Huang, Yueh-Min

2012-01-01

A growing number of educational studies apply sensors to improve student learning in real classroom settings. However, how can sensors be integrated into classrooms to help instructors find out students' reading concentration rates and thus better increase learning effectiveness? The aim of the current study was to develop a reading concentration monitoring system for use with e-books in an intelligent classroom and to help instructors find out the students' reading concentration rates. The proposed system uses three types of sensor technologies, namely a webcam, heartbeat sensor, and blood oxygen sensor to detect the learning behaviors of students by capturing various physiological signals. An artificial bee colony (ABC) optimization approach is applied to the data gathered from these sensors to help instructors understand their students' reading concentration rates in a classroom learning environment. The results show that the use of the ABC algorithm in the proposed system can effectively obtain near-optimal solutions. The system has a user-friendly graphical interface, making it easy for instructors to clearly understand the reading status of their students. PMID:23202042

Metaheuristic optimization approaches to predict shear-wave velocity from conventional well logs in sandstone and carbonate case studies

NASA Astrophysics Data System (ADS)

Emami Niri, Mohammad; Amiri Kolajoobi, Rasool; Khodaiy Arbat, Mohammad; Shahbazi Raz, Mahdi

2018-06-01

Seismic wave velocities, along with petrophysical data, provide valuable information during the exploration and development stages of oil and gas fields. The compressional-wave velocity (VP ) is acquired using conventional acoustic logging tools in many drilled wells. But the shear-wave velocity (VS ) is recorded using advanced logging tools only in a limited number of wells, mainly because of the high operational costs. In addition, laboratory measurements of seismic velocities on core samples are expensive and time consuming. So, alternative methods are often used to estimate VS . Heretofore, several empirical correlations that predict VS by using well logging measurements and petrophysical data such as VP , porosity and density are proposed. However, these empirical relations can only be used in limited cases. The use of intelligent systems and optimization algorithms are inexpensive, fast and efficient approaches for predicting VS. In this study, in addition to the widely used Greenberg–Castagna empirical method, we implement three relatively recently developed metaheuristic algorithms to construct linear and nonlinear models for predicting VS : teaching–learning based optimization, imperialist competitive and artificial bee colony algorithms. We demonstrate the applicability and performance of these algorithms to predict Vs using conventional well logs in two field data examples, a sandstone formation from an offshore oil field and a carbonate formation from an onshore oil field. We compared the estimated VS using each of the employed metaheuristic approaches with observed VS and also with those predicted by Greenberg–Castagna relations. The results indicate that, for both sandstone and carbonate case studies, all three implemented metaheuristic algorithms are more efficient and reliable than the empirical correlation to predict VS . The results also demonstrate that in both sandstone and carbonate case studies, the performance of an artificial bee colony algorithm in VS prediction is slightly higher than two other alternative employed approaches.

Optimal Cloning of PCR Fragments by Homologous Recombination in Escherichia coli

PubMed Central

Jacobus, Ana Paula; Gross, Jeferson

2015-01-01

PCR fragments and linear vectors containing overlapping ends are easily assembled into a propagative plasmid by homologous recombination in Escherichia coli. Although this gap-repair cloning approach is straightforward, its existence is virtually unknown to most molecular biologists. To popularize this method, we tested critical parameters influencing the efficiency of PCR fragments cloning into PCR-amplified vectors by homologous recombination in the widely used E. coli strain DH5α. We found that the number of positive colonies after transformation increases with the length of overlap between the PCR fragment and linear vector. For most practical purposes, a 20 bp identity already ensures high-cloning yields. With an insert to vector ratio of 2:1, higher colony forming numbers are obtained when the amount of vector is in the range of 100 to 250 ng. An undesirable cloning background of empty vectors can be minimized during vector PCR amplification by applying a reduced amount of plasmid template or by using primers in which the 5′ termini are separated by a large gap. DpnI digestion of the plasmid template after PCR is also effective to decrease the background of negative colonies. We tested these optimized cloning parameters during the assembly of five independent DNA constructs and obtained 94% positive clones out of 100 colonies probed. We further demonstrated the efficient and simultaneous cloning of two PCR fragments into a vector. These results support the idea that homologous recombination in E. coli might be one of the most effective methods for cloning one or two PCR fragments. For its simplicity and high efficiency, we believe that recombinational cloning in E. coli has a great potential to become a routine procedure in most molecular biology-oriented laboratories. PMID:25774528

Solving NP-Hard Problems with Physarum-Based Ant Colony System.

PubMed

Liu, Yuxin; Gao, Chao; Zhang, Zili; Lu, Yuxiao; Chen, Shi; Liang, Mingxin; Tao, Li

2017-01-01

NP-hard problems exist in many real world applications. Ant colony optimization (ACO) algorithms can provide approximate solutions for those NP-hard problems, but the performance of ACO algorithms is significantly reduced due to premature convergence and weak robustness, etc. With these observations in mind, this paper proposes a Physarum-based pheromone matrix optimization strategy in ant colony system (ACS) for solving NP-hard problems such as traveling salesman problem (TSP) and 0/1 knapsack problem (0/1 KP). In the Physarum-inspired mathematical model, one of the unique characteristics is that critical tubes can be reserved in the process of network evolution. The optimized updating strategy employs the unique feature and accelerates the positive feedback process in ACS, which contributes to the quick convergence of the optimal solution. Some experiments were conducted using both benchmark and real datasets. The experimental results show that the optimized ACS outperforms other meta-heuristic algorithms in accuracy and robustness for solving TSPs. Meanwhile, the convergence rate and robustness for solving 0/1 KPs are better than those of classical ACS.

Ant algorithms for discrete optimization.

PubMed

Dorigo, M; Di Caro, G; Gambardella, L M

1999-01-01

This article presents an overview of recent work on ant algorithms, that is, algorithms for discrete optimization that took inspiration from the observation of ant colonies' foraging behavior, and introduces the ant colony optimization (ACO) metaheuristic. In the first part of the article the basic biological findings on real ants are reviewed and their artificial counterparts as well as the ACO metaheuristic are defined. In the second part of the article a number of applications of ACO algorithms to combinatorial optimization and routing in communications networks are described. We conclude with a discussion of related work and of some of the most important aspects of the ACO metaheuristic.

Experimental Study for Automatic Colony Counting System Based Onimage Processing

NASA Astrophysics Data System (ADS)

Fang, Junlong; Li, Wenzhe; Wang, Guoxin

Colony counting in many colony experiments is detected by manual method at present, therefore it is difficult for man to execute the method quickly and accurately .A new automatic colony counting system was developed. Making use of image-processing technology, a study was made on the feasibility of distinguishing objectively white bacterial colonies from clear plates according to the RGB color theory. An optimal chromatic value was obtained based upon a lot of experiments on the distribution of the chromatic value. It has been proved that the method greatly improves the accuracy and efficiency of the colony counting and the counting result is not affected by using inoculation, shape or size of the colony. It is revealed that automatic detection of colony quantity using image-processing technology could be an effective way.

Dual-threshold segmentation using Arimoto entropy based on chaotic bee colony optimization

NASA Astrophysics Data System (ADS)

Li, Li

2018-03-01

In order to extract target from complex background more quickly and accurately, and to further improve the detection effect of defects, a method of dual-threshold segmentation using Arimoto entropy based on chaotic bee colony optimization was proposed. Firstly, the method of single-threshold selection based on Arimoto entropy was extended to dual-threshold selection in order to separate the target from the background more accurately. Then intermediate variables in formulae of Arimoto entropy dual-threshold selection was calculated by recursion to eliminate redundant computation effectively and to reduce the amount of calculation. Finally, the local search phase of artificial bee colony algorithm was improved by chaotic sequence based on tent mapping. The fast search for two optimal thresholds was achieved using the improved bee colony optimization algorithm, thus the search could be accelerated obviously. A large number of experimental results show that, compared with the existing segmentation methods such as multi-threshold segmentation method using maximum Shannon entropy, two-dimensional Shannon entropy segmentation method, two-dimensional Tsallis gray entropy segmentation method and multi-threshold segmentation method using reciprocal gray entropy, the proposed method can segment target more quickly and accurately with superior segmentation effect. It proves to be an instant and effective method for image segmentation.

Analysis of parameter estimation and optimization application of ant colony algorithm in vehicle routing problem

NASA Astrophysics Data System (ADS)

Xu, Quan-Li; Cao, Yu-Wei; Yang, Kun

2018-03-01

Ant Colony Optimization (ACO) is the most widely used artificial intelligence algorithm at present. This study introduced the principle and mathematical model of ACO algorithm in solving Vehicle Routing Problem (VRP), and designed a vehicle routing optimization model based on ACO, then the vehicle routing optimization simulation system was developed by using c ++ programming language, and the sensitivity analyses, estimations and improvements of the three key parameters of ACO were carried out. The results indicated that the ACO algorithm designed in this paper can efficiently solve rational planning and optimization of VRP, and the different values of the key parameters have significant influence on the performance and optimization effects of the algorithm, and the improved algorithm is not easy to locally converge prematurely and has good robustness.

Enhancing artificial bee colony algorithm with self-adaptive searching strategy and artificial immune network operators for global optimization.

PubMed

Chen, Tinggui; Xiao, Renbin

2014-01-01

Artificial bee colony (ABC) algorithm, inspired by the intelligent foraging behavior of honey bees, was proposed by Karaboga. It has been shown to be superior to some conventional intelligent algorithms such as genetic algorithm (GA), artificial colony optimization (ACO), and particle swarm optimization (PSO). However, the ABC still has some limitations. For example, ABC can easily get trapped in the local optimum when handing in functions that have a narrow curving valley, a high eccentric ellipse, or complex multimodal functions. As a result, we proposed an enhanced ABC algorithm called EABC by introducing self-adaptive searching strategy and artificial immune network operators to improve the exploitation and exploration. The simulation results tested on a suite of unimodal or multimodal benchmark functions illustrate that the EABC algorithm outperforms ACO, PSO, and the basic ABC in most of the experiments.

Enhancing Artificial Bee Colony Algorithm with Self-Adaptive Searching Strategy and Artificial Immune Network Operators for Global Optimization

PubMed Central

Chen, Tinggui; Xiao, Renbin

2014-01-01

Artificial bee colony (ABC) algorithm, inspired by the intelligent foraging behavior of honey bees, was proposed by Karaboga. It has been shown to be superior to some conventional intelligent algorithms such as genetic algorithm (GA), artificial colony optimization (ACO), and particle swarm optimization (PSO). However, the ABC still has some limitations. For example, ABC can easily get trapped in the local optimum when handing in functions that have a narrow curving valley, a high eccentric ellipse, or complex multimodal functions. As a result, we proposed an enhanced ABC algorithm called EABC by introducing self-adaptive searching strategy and artificial immune network operators to improve the exploitation and exploration. The simulation results tested on a suite of unimodal or multimodal benchmark functions illustrate that the EABC algorithm outperforms ACO, PSO, and the basic ABC in most of the experiments. PMID:24772023

Swarm Intelligence Optimization and Its Applications

NASA Astrophysics Data System (ADS)

Ding, Caichang; Lu, Lu; Liu, Yuanchao; Peng, Wenxiu

Swarm Intelligence is a computational and behavioral metaphor for solving distributed problems inspired from biological examples provided by social insects such as ants, termites, bees, and wasps and by swarm, herd, flock, and shoal phenomena in vertebrates such as fish shoals and bird flocks. An example of successful research direction in Swarm Intelligence is ant colony optimization (ACO), which focuses on combinatorial optimization problems. Ant algorithms can be viewed as multi-agent systems (ant colony), where agents (individual ants) solve required tasks through cooperation in the same way that ants create complex social behavior from the combined efforts of individuals.

Large Scale Bacterial Colony Screening of Diversified FRET Biosensors

PubMed Central

Litzlbauer, Julia; Schifferer, Martina; Ng, David; Fabritius, Arne; Thestrup, Thomas; Griesbeck, Oliver

2015-01-01

Biosensors based on Förster Resonance Energy Transfer (FRET) between fluorescent protein mutants have started to revolutionize physiology and biochemistry. However, many types of FRET biosensors show relatively small FRET changes, making measurements with these probes challenging when used under sub-optimal experimental conditions. Thus, a major effort in the field currently lies in designing new optimization strategies for these types of sensors. Here we describe procedures for optimizing FRET changes by large scale screening of mutant biosensor libraries in bacterial colonies. We describe optimization of biosensor expression, permeabilization of bacteria, software tools for analysis, and screening conditions. The procedures reported here may help in improving FRET changes in multiple suitable classes of biosensors. PMID:26061878

Multiple-hopping trajectories near a rotating asteroid

NASA Astrophysics Data System (ADS)

Shen, Hong-Xin; Zhang, Tian-Jiao; Li, Zhao; Li, Heng-Nian

2017-03-01

We present a study of the transfer orbits connecting landing points of irregular-shaped asteroids. The landing points do not touch the surface of the asteroids and are chosen several meters above the surface. The ant colony optimization technique is used to calculate the multiple-hopping trajectories near an arbitrary irregular asteroid. This new method has three steps which are as follows: (1) the search of the maximal clique of candidate target landing points; (2) leg optimization connecting all landing point pairs; and (3) the hopping sequence optimization. In particular this method is applied to asteroids 433 Eros and 216 Kleopatra. We impose a critical constraint on the target landing points to allow for extensive exploration of the asteroid: the relative distance between all the arrived target positions should be larger than a minimum allowed value. Ant colony optimization is applied to find the set and sequence of targets, and the differential evolution algorithm is used to solve for the hopping orbits. The minimum-velocity increment tours of hopping trajectories connecting all the landing positions are obtained by ant colony optimization. The results from different size asteroids indicate that the cost of the minimum velocity-increment tour depends on the size of the asteroids.

An Ant Colony Optimization and Hybrid Metaheuristics Algorithm to Solve the Split Delivery Vehicle Routing Problem

DTIC Science & Technology

2015-01-01

programming formulation of traveling salesman problems , Journal of the ACM, 7(4), 326-329. Montemanni, R., Gambardella, L. M., Rizzoli, A.E., Donati. A.V... salesman problem . BioSystem, 43(1), 73-81. Dror, M., Trudeau, P., 1989. Savings by split delivery routing. Transportation Science, 23, 141- 145. Dror, M...An Ant Colony Optimization and Hybrid Metaheuristics Algorithm to solve the Split Delivery Vehicle Routing Problem Authors: Gautham Rajappa

Routing and spectrum assignment based on ant colony optimization of minimum consecutiveness loss in elastic optical networks

NASA Astrophysics Data System (ADS)

Wang, Fu; Liu, Bo; Zhang, Lijia; Xin, Xiangjun; Tian, Qinghua; Zhang, Qi; Rao, Lan; Tian, Feng; Luo, Biao; Liu, Yingjun; Tang, Bao

2016-10-01

Elastic Optical Networks are considered to be a promising technology for future high-speed network. In this paper, we propose a RSA algorithm based on the ant colony optimization of minimum consecutiveness loss (ACO-MCL). Based on the effect of the spectrum consecutiveness loss on the pheromone in the ant colony optimization, the path and spectrum of the minimal impact on the network are selected for the service request. When an ant arrives at the destination node from the source node along a path, we assume that this path is selected for the request. We calculate the consecutiveness loss of candidate-neighbor link pairs along this path after the routing and spectrum assignment. Then, the networks update the pheromone according to the value of the consecutiveness loss. We save the path with the smallest value. After multiple iterations of the ant colony optimization, the final selection of the path is assigned for the request. The algorithms are simulated in different networks. The results show that ACO-MCL algorithm performs better in blocking probability and spectrum efficiency than other algorithms. Moreover, the ACO-MCL algorithm can effectively decrease spectrum fragmentation and enhance available spectrum consecutiveness. Compared with other algorithms, the ACO-MCL algorithm can reduce the blocking rate by at least 5.9% in heavy load.

Estimating rates of local extinction and colonization in colonial species and an extension to the metapopulation and community levels

USGS Publications Warehouse

Barbraud, C.; Nichols, J.D.; Hines, J.E.; Hafner, H.

2003-01-01

Coloniality has mainly been studied from an evolutionary perspective, but relatively few studies have developed methods for modelling colony dynamics. Changes in number of colonies over time provide a useful tool for predicting and evaluating the responses of colonial species to management and to environmental disturbance. Probabilistic Markov process models have been recently used to estimate colony site dynamics using presence-absence data when all colonies are detected in sampling efforts. Here, we define and develop two general approaches for the modelling and analysis of colony dynamics for sampling situations in which all colonies are, and are not, detected. For both approaches, we develop a general probabilistic model for the data and then constrain model parameters based on various hypotheses about colony dynamics. We use Akaike's Information Criterion (AIC) to assess the adequacy of the constrained models. The models are parameterised with conditional probabilities of local colony site extinction and colonization. Presence-absence data arising from Pollock's robust capture-recapture design provide the basis for obtaining unbiased estimates of extinction, colonization, and detection probabilities when not all colonies are detected. This second approach should be particularly useful in situations where detection probabilities are heterogeneous among colony sites. The general methodology is illustrated using presence-absence data on two species of herons (Purple Heron, Ardea purpurea and Grey Heron, Ardea cinerea). Estimates of the extinction and colonization rates showed interspecific differences and strong temporal and spatial variations. We were also able to test specific predictions about colony dynamics based on ideas about habitat change and metapopulation dynamics. We recommend estimators based on probabilistic modelling for future work on colony dynamics. We also believe that this methodological framework has wide application to problems in animal ecology concerning metapopulation and community dynamics.

Modeling Design Iteration in Product Design and Development and Its Solution by a Novel Artificial Bee Colony Algorithm

PubMed Central

2014-01-01

Due to fierce market competition, how to improve product quality and reduce development cost determines the core competitiveness of enterprises. However, design iteration generally causes increases of product cost and delays of development time as well, so how to identify and model couplings among tasks in product design and development has become an important issue for enterprises to settle. In this paper, the shortcomings existing in WTM model are discussed and tearing approach as well as inner iteration method is used to complement the classic WTM model. In addition, the ABC algorithm is also introduced to find out the optimal decoupling schemes. In this paper, firstly, tearing approach and inner iteration method are analyzed for solving coupled sets. Secondly, a hybrid iteration model combining these two technologies is set up. Thirdly, a high-performance swarm intelligence algorithm, artificial bee colony, is adopted to realize problem-solving. Finally, an engineering design of a chemical processing system is given in order to verify its reasonability and effectiveness. PMID:25431584

Approaches and Challenges to Managing Nosema (Microspora: Nosematidae) Parasites in Honey Bee (Hymenoptera: Apidae) Colonies.

PubMed

Holt, Holly L; Grozinger, Christina M

2016-08-01

The microsporidia Nosema apis (Zander) and Nosema ceranae (Fries) are common intestinal parasites in honey bee (Apis mellifera L.) colonies. Though globally prevalent, there are mixed reports of Nosema infection costs, with some regions reporting high parasite virulence and colony losses, while others high Nosema prevalence but few costs. Basic and applied studies are urgently needed to help beekeepers effectively manage Nosema spp., ideally through an integrated pest management approach that allows beekeepers to deploy multiple strategies to control Nosema when Nosema is likely to cause damage to the colonies, rather than using prophylactic treatments. Beekeepers need practical and affordable technologies that facilitate disease diagnosis and science-backed guidelines that recommend when, if at all, to treat infections. In addition, new treatment methods are needed, as there are several problems associated with the chemical use of fumagillin (the only currently extensively studied, but not globally available treatment) to control Nosema parasites. Though selective breeding of Nosema-resistant or tolerant bees may offer a long-term, sustainable solution to Nosema management, other treatments are needed in the interim. Furthermore, the validation of alternative treatment efficacy in field settings is needed along with toxicology assays to ensure that treatments do not have unintended, adverse effects on honey bees or humans. Finally, given variation in Nosema virulence, development of regional management guidelines, rather than universal guidelines, may provide optimal and cost-effective Nosema management, though more research is needed before regional plans can be developed. © The Authors 2016. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

MATLAB Simulation of UPQC for Power Quality Mitigation Using an Ant Colony Based Fuzzy Control Technique

PubMed Central

Kumarasabapathy, N.; Manoharan, P. S.

2015-01-01

This paper proposes a fuzzy logic based new control scheme for the Unified Power Quality Conditioner (UPQC) for minimizing the voltage sag and total harmonic distortion in the distribution system consequently to improve the power quality. UPQC is a recent power electronic module which guarantees better power quality mitigation as it has both series-active and shunt-active power filters (APFs). The fuzzy logic controller has recently attracted a great deal of attention and possesses conceptually the quality of the simplicity by tackling complex systems with vagueness and ambiguity. In this research, the fuzzy logic controller is utilized for the generation of reference signal controlling the UPQC. To enable this, a systematic approach for creating the fuzzy membership functions is carried out by using an ant colony optimization technique for optimal fuzzy logic control. An exhaustive simulation study using the MATLAB/Simulink is carried out to investigate and demonstrate the performance of the proposed fuzzy logic controller and the simulation results are compared with the PI controller in terms of its performance in improving the power quality by minimizing the voltage sag and total harmonic distortion. PMID:26504895

Termites: a Retinex implementation based on a colony of agents

NASA Astrophysics Data System (ADS)

Simone, Gabriele; Audino, Giuseppe; Farup, Ivar; Rizzi, Alessandro

2012-01-01

This paper describes a novel implementation of the Retinex algorithm with the exploration of the image done by an ant swarm. In this case the purpose of the ant colony is not the optimization of some constraints but is an alternative way to explore the image content as diffused as possible, with the possibility of tuning the exploration parameters to the image content trying to better approach the Human Visual System behavior. For this reason, we used "termites", instead of ants, to underline the idea of the eager exploration of the image. The paper presents the spatial characteristics of locality and discusses differences in path exploration with other Retinex implementations. Furthermore a psychophysical experiment has been carried out on eight images with 20 observers and results indicate that a termite swarm should investigate a particular region of an image to find the local reference white.

Colony, hanging drop, and methylcellulose three dimensional hypoxic growth optimization of renal cell carcinoma cell lines.

PubMed

Matak, Damian; Brodaczewska, Klaudia K; Lipiec, Monika; Szymanski, Łukasz; Szczylik, Cezary; Czarnecka, Anna M

2017-08-01

Renal cell carcinoma (RCC) is the most lethal of the common urologic malignancies, comprising 3% of all human neoplasms, and the incidence of kidney cancer is rising annually. We need new approaches to target tumor cells that are resistant to current therapies and that give rise to recurrence and treatment failure. In this study, we focused on low oxygen tension and three-dimensional (3D) cell culture incorporation to develop a new RCC growth model. We used the hanging drop and colony formation methods, which are common in 3D culture, as well as a unique methylcellulose (MC) method. For the experiments, we used human primary RCC cell lines, metastatic RCC cell lines, human kidney cancer stem cells, and human healthy epithelial cells. In the hanging drop assay, we verified the potential of various cell lines to create solid aggregates in hypoxic and normoxic conditions. With the semi-soft agar method, we also determined the ability of various cell lines to create colonies under different oxygen conditions. Different cell behavior observed in the MC method versus the hanging drop and colony formation assays suggests that these three assays may be useful to test various cell properties. However, MC seems to be a particularly valuable alternative for 3D cell culture, as its higher efficiency of aggregate formation and serum independency are of interest in different areas of cancer biology.

Genetic Bee Colony (GBC) algorithm: A new gene selection method for microarray cancer classification.

PubMed

Alshamlan, Hala M; Badr, Ghada H; Alohali, Yousef A

2015-06-01

Naturally inspired evolutionary algorithms prove effectiveness when used for solving feature selection and classification problems. Artificial Bee Colony (ABC) is a relatively new swarm intelligence method. In this paper, we propose a new hybrid gene selection method, namely Genetic Bee Colony (GBC) algorithm. The proposed algorithm combines the used of a Genetic Algorithm (GA) along with Artificial Bee Colony (ABC) algorithm. The goal is to integrate the advantages of both algorithms. The proposed algorithm is applied to a microarray gene expression profile in order to select the most predictive and informative genes for cancer classification. In order to test the accuracy performance of the proposed algorithm, extensive experiments were conducted. Three binary microarray datasets are use, which include: colon, leukemia, and lung. In addition, another three multi-class microarray datasets are used, which are: SRBCT, lymphoma, and leukemia. Results of the GBC algorithm are compared with our recently proposed technique: mRMR when combined with the Artificial Bee Colony algorithm (mRMR-ABC). We also compared the combination of mRMR with GA (mRMR-GA) and Particle Swarm Optimization (mRMR-PSO) algorithms. In addition, we compared the GBC algorithm with other related algorithms that have been recently published in the literature, using all benchmark datasets. The GBC algorithm shows superior performance as it achieved the highest classification accuracy along with the lowest average number of selected genes. This proves that the GBC algorithm is a promising approach for solving the gene selection problem in both binary and multi-class cancer classification. Copyright © 2015 Elsevier Ltd. All rights reserved.

An intelligent identification algorithm for the monoclonal picking instrument

NASA Astrophysics Data System (ADS)

Yan, Hua; Zhang, Rongfu; Yuan, Xujun; Wang, Qun

2017-11-01

The traditional colony selection is mainly operated by manual mode, which takes on low efficiency and strong subjectivity. Therefore, it is important to develop an automatic monoclonal-picking instrument. The critical stage of the automatic monoclonal-picking and intelligent optimal selection is intelligent identification algorithm. An auto-screening algorithm based on Support Vector Machine (SVM) is proposed in this paper, which uses the supervised learning method, which combined with the colony morphological characteristics to classify the colony accurately. Furthermore, through the basic morphological features of the colony, system can figure out a series of morphological parameters step by step. Through the establishment of maximal margin classifier, and based on the analysis of the growth trend of the colony, the selection of the monoclonal colony was carried out. The experimental results showed that the auto-screening algorithm could screen out the regular colony from the other, which meets the requirement of various parameters.

Framework for computationally efficient optimal irrigation scheduling using ant colony optimization

USDA-ARS?s Scientific Manuscript database

A general optimization framework is introduced with the overall goal of reducing search space size and increasing the computational efficiency of evolutionary algorithm application for optimal irrigation scheduling. The framework achieves this goal by representing the problem in the form of a decisi...

A Multiuser Detector Based on Artificial Bee Colony Algorithm for DS-UWB Systems

PubMed Central

Liu, Xiaohui

2013-01-01

Artificial Bee Colony (ABC) algorithm is an optimization algorithm based on the intelligent behavior of honey bee swarm. The ABC algorithm was developed to solve optimizing numerical problems and revealed premising results in processing time and solution quality. In ABC, a colony of artificial bees search for rich artificial food sources; the optimizing numerical problems are converted to the problem of finding the best parameter which minimizes an objective function. Then, the artificial bees randomly discover a population of initial solutions and then iteratively improve them by employing the behavior: moving towards better solutions by means of a neighbor search mechanism while abandoning poor solutions. In this paper, an efficient multiuser detector based on a suboptimal code mapping multiuser detector and artificial bee colony algorithm (SCM-ABC-MUD) is proposed and implemented in direct-sequence ultra-wideband (DS-UWB) systems under the additive white Gaussian noise (AWGN) channel. The simulation results demonstrate that the BER and the near-far effect resistance performances of this proposed algorithm are quite close to those of the optimum multiuser detector (OMD) while its computational complexity is much lower than that of OMD. Furthermore, the BER performance of SCM-ABC-MUD is not sensitive to the number of active users and can obtain a large system capacity. PMID:23983638

Fairness in optimizing bus-crew scheduling process.

PubMed

Ma, Jihui; Song, Cuiying; Ceder, Avishai Avi; Liu, Tao; Guan, Wei

2017-01-01

This work proposes a model considering fairness in the problem of crew scheduling for bus drivers (CSP-BD) using a hybrid ant-colony optimization (HACO) algorithm to solve it. The main contributions of this work are the following: (a) a valid approach for cases with a special cost structure and constraints considering the fairness of working time and idle time; (b) an improved algorithm incorporating Gamma heuristic function and selecting rules. The relationships of each cost are examined with ten bus lines collected from the Beijing Public Transport Holdings (Group) Co., Ltd., one of the largest bus transit companies in the world. It shows that unfair cost is indirectly related to common cost, fixed cost and extra cost and also the unfair cost approaches to common and fixed cost when its coefficient is twice of common cost coefficient. Furthermore, the longest time for the tested bus line with 1108 pieces, 74 blocks is less than 30 minutes. The results indicate that the HACO-based algorithm can be a feasible and efficient optimization technique for CSP-BD, especially with large scale problems.

Lazy workers are necessary for long-term sustainability in insect societies

PubMed Central

Hasegawa, Eisuke; Ishii, Yasunori; Tada, Koichiro; Kobayashi, Kazuya; Yoshimura, Jin

2016-01-01

Optimality theory predicts the maximization of productivity in social insect colonies, but many inactive workers are found in ant colonies. Indeed, the low short-term productivity of ant colonies is often the consequence of high variation among workers in the threshold to respond to task-related stimuli. Why is such an inefficient strategy among colonies maintained by natural selection? Here, we show that inactive workers are necessary for the long-term sustainability of a colony. Our simulation shows that colonies with variable thresholds persist longer than those with invariable thresholds because inactive workers perform the critical function of replacing active workers when they become fatigued. Evidence of the replacement of active workers by inactive workers has been found in ant colonies. Thus, the presence of inactive workers increases the long-term persistence of the colony at the expense of decreasing short-term productivity. Inactive workers may represent a bet-hedging strategy in response to environmental stochasticity. PMID:26880339

An efficient algorithm for function optimization: modified stem cells algorithm

NASA Astrophysics Data System (ADS)

Taherdangkoo, Mohammad; Paziresh, Mahsa; Yazdi, Mehran; Bagheri, Mohammad Hadi

2013-03-01

In this paper, we propose an optimization algorithm based on the intelligent behavior of stem cell swarms in reproduction and self-organization. Optimization algorithms, such as the Genetic Algorithm (GA), Particle Swarm Optimization (PSO) algorithm, Ant Colony Optimization (ACO) algorithm and Artificial Bee Colony (ABC) algorithm, can give solutions to linear and non-linear problems near to the optimum for many applications; however, in some case, they can suffer from becoming trapped in local optima. The Stem Cells Algorithm (SCA) is an optimization algorithm inspired by the natural behavior of stem cells in evolving themselves into new and improved cells. The SCA avoids the local optima problem successfully. In this paper, we have made small changes in the implementation of this algorithm to obtain improved performance over previous versions. Using a series of benchmark functions, we assess the performance of the proposed algorithm and compare it with that of the other aforementioned optimization algorithms. The obtained results prove the superiority of the Modified Stem Cells Algorithm (MSCA).

Multimodal optimization by using hybrid of artificial bee colony algorithm and BFGS algorithm

NASA Astrophysics Data System (ADS)

Anam, S.

2017-10-01

Optimization has become one of the important fields in Mathematics. Many problems in engineering and science can be formulated into optimization problems. They maybe have many local optima. The optimization problem with many local optima, known as multimodal optimization problem, is how to find the global solution. Several metaheuristic methods have been proposed to solve multimodal optimization problems such as Particle Swarm Optimization (PSO), Genetics Algorithm (GA), Artificial Bee Colony (ABC) algorithm, etc. The performance of the ABC algorithm is better than or similar to those of other population-based algorithms with the advantage of employing a fewer control parameters. The ABC algorithm also has the advantages of strong robustness, fast convergence and high flexibility. However, it has the disadvantages premature convergence in the later search period. The accuracy of the optimal value cannot meet the requirements sometimes. Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm is a good iterative method for finding a local optimum. Compared with other local optimization methods, the BFGS algorithm is better. Based on the advantages of the ABC algorithm and the BFGS algorithm, this paper proposes a hybrid of the artificial bee colony algorithm and the BFGS algorithm to solve the multimodal optimization problem. The first step is that the ABC algorithm is run to find a point. In the second step is that the point obtained by the first step is used as an initial point of BFGS algorithm. The results show that the hybrid method can overcome from the basic ABC algorithm problems for almost all test function. However, if the shape of function is flat, the proposed method cannot work well.

Ant Colony Optimization for Markowitz Mean-Variance Portfolio Model

NASA Astrophysics Data System (ADS)

Deng, Guang-Feng; Lin, Woo-Tsong

This work presents Ant Colony Optimization (ACO), which was initially developed to be a meta-heuristic for combinatorial optimization, for solving the cardinality constraints Markowitz mean-variance portfolio model (nonlinear mixed quadratic programming problem). To our knowledge, an efficient algorithmic solution for this problem has not been proposed until now. Using heuristic algorithms in this case is imperative. Numerical solutions are obtained for five analyses of weekly price data for the following indices for the period March, 1992 to September, 1997: Hang Seng 31 in Hong Kong, DAX 100 in Germany, FTSE 100 in UK, S&P 100 in USA and Nikkei 225 in Japan. The test results indicate that the ACO is much more robust and effective than Particle swarm optimization (PSO), especially for low-risk investment portfolios.

Parallelizing Ant Colony Optimization via Area of Expertise Learning

DTIC Science & Technology

2007-09-13

reminding me that when you don’t have anything else to say, a cat on the keyboard can at least get you a few more pages. Adrian A. de Freitas v Table of...Determination of Others’ Expertise (Q-Learning) . . . . . . . . 69 viii Figure Page 4.8. Pheromone Concentrations of Colonies 1, 2, and 3... Pheromone Concentrations of a Single ACS-GRIDWORLD Colony 75 4.14. Comparison of Policies Formed through AOE Learning . . . . . 75 4.15. Combined Pheromone

An ant colony optimization based algorithm for identifying gene regulatory elements.

PubMed

Liu, Wei; Chen, Hanwu; Chen, Ling

2013-08-01

It is one of the most important tasks in bioinformatics to identify the regulatory elements in gene sequences. Most of the existing algorithms for identifying regulatory elements are inclined to converge into a local optimum, and have high time complexity. Ant Colony Optimization (ACO) is a meta-heuristic method based on swarm intelligence and is derived from a model inspired by the collective foraging behavior of real ants. Taking advantage of the ACO in traits such as self-organization and robustness, this paper designs and implements an ACO based algorithm named ACRI (ant-colony-regulatory-identification) for identifying all possible binding sites of transcription factor from the upstream of co-expressed genes. To accelerate the ants' searching process, a strategy of local optimization is presented to adjust the ants' start positions on the searched sequences. By exploiting the powerful optimization ability of ACO, the algorithm ACRI can not only improve precision of the results, but also achieve a very high speed. Experimental results on real world datasets show that ACRI can outperform other traditional algorithms in the respects of speed and quality of solutions. Copyright © 2013 Elsevier Ltd. All rights reserved.

A stereo remote sensing feature selection method based on artificial bee colony algorithm

NASA Astrophysics Data System (ADS)

Yan, Yiming; Liu, Pigang; Zhang, Ye; Su, Nan; Tian, Shu; Gao, Fengjiao; Shen, Yi

2014-05-01

To improve the efficiency of stereo information for remote sensing classification, a stereo remote sensing feature selection method is proposed in this paper presents, which is based on artificial bee colony algorithm. Remote sensing stereo information could be described by digital surface model (DSM) and optical image, which contain information of the three-dimensional structure and optical characteristics, respectively. Firstly, three-dimensional structure characteristic could be analyzed by 3D-Zernike descriptors (3DZD). However, different parameters of 3DZD could descript different complexity of three-dimensional structure, and it needs to be better optimized selected for various objects on the ground. Secondly, features for representing optical characteristic also need to be optimized. If not properly handled, when a stereo feature vector composed of 3DZD and image features, that would be a lot of redundant information, and the redundant information may not improve the classification accuracy, even cause adverse effects. To reduce information redundancy while maintaining or improving the classification accuracy, an optimized frame for this stereo feature selection problem is created, and artificial bee colony algorithm is introduced for solving this optimization problem. Experimental results show that the proposed method can effectively improve the computational efficiency, improve the classification accuracy.

Cloud computing task scheduling strategy based on differential evolution and ant colony optimization

NASA Astrophysics Data System (ADS)

Ge, Junwei; Cai, Yu; Fang, Yiqiu

2018-05-01

This paper proposes a task scheduling strategy DEACO based on the combination of Differential Evolution (DE) and Ant Colony Optimization (ACO), aiming at the single problem of optimization objective in cloud computing task scheduling, this paper combines the shortest task completion time, cost and load balancing. DEACO uses the solution of the DE to initialize the initial pheromone of ACO, reduces the time of collecting the pheromone in ACO in the early, and improves the pheromone updating rule through the load factor. The proposed algorithm is simulated on cloudsim, and compared with the min-min and ACO. The experimental results show that DEACO is more superior in terms of time, cost, and load.

Optimization research of railway passenger transfer scheme based on ant colony algorithm

NASA Astrophysics Data System (ADS)

Ni, Xiang

2018-05-01

The optimization research of railway passenger transfer scheme can provide strong support for railway passenger transport system, and its essence is path search. This paper realized the calculation of passenger transfer scheme for high speed railway when giving the time and stations of departure and arrival. The specific method that used were generating a passenger transfer service network of high-speed railway, establishing optimization model and searching by Ant Colony Algorithm. Finally, making analysis on the scheme from LanZhouxi to BeiJingXi which were based on high-speed railway network of China in 2017. The results showed that the transfer network and model had relatively high practical value and operation efficiency.

A global optimization algorithm inspired in the behavior of selfish herds.

PubMed

Fausto, Fernando; Cuevas, Erik; Valdivia, Arturo; González, Adrián

2017-10-01

In this paper, a novel swarm optimization algorithm called the Selfish Herd Optimizer (SHO) is proposed for solving global optimization problems. SHO is based on the simulation of the widely observed selfish herd behavior manifested by individuals within a herd of animals subjected to some form of predation risk. In SHO, individuals emulate the predatory interactions between groups of prey and predators by two types of search agents: the members of a selfish herd (the prey) and a pack of hungry predators. Depending on their classification as either a prey or a predator, each individual is conducted by a set of unique evolutionary operators inspired by such prey-predator relationship. These unique traits allow SHO to improve the balance between exploration and exploitation without altering the population size. To illustrate the proficiency and robustness of the proposed method, it is compared to other well-known evolutionary optimization approaches such as Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Firefly Algorithm (FA), Differential Evolution (DE), Genetic Algorithms (GA), Crow Search Algorithm (CSA), Dragonfly Algorithm (DA), Moth-flame Optimization Algorithm (MOA) and Sine Cosine Algorithm (SCA). The comparison examines several standard benchmark functions, commonly considered within the literature of evolutionary algorithms. The experimental results show the remarkable performance of our proposed approach against those of the other compared methods, and as such SHO is proven to be an excellent alternative to solve global optimization problems. Copyright © 2017 Elsevier B.V. All rights reserved.

All-Optical Implementation of the Ant Colony Optimization Algorithm

PubMed Central

Hu, Wenchao; Wu, Kan; Shum, Perry Ping; Zheludev, Nikolay I.; Soci, Cesare

2016-01-01

We report all-optical implementation of the optimization algorithm for the famous “ant colony” problem. Ant colonies progressively optimize pathway to food discovered by one of the ants through identifying the discovered route with volatile chemicals (pheromones) secreted on the way back from the food deposit. Mathematically this is an important example of graph optimization problem with dynamically changing parameters. Using an optical network with nonlinear waveguides to represent the graph and a feedback loop, we experimentally show that photons traveling through the network behave like ants that dynamically modify the environment to find the shortest pathway to any chosen point in the graph. This proof-of-principle demonstration illustrates how transient nonlinearity in the optical system can be exploited to tackle complex optimization problems directly, on the hardware level, which may be used for self-routing of optical signals in transparent communication networks and energy flow in photonic systems. PMID:27222098

Modified artificial bee colony algorithm for reactive power optimization

NASA Astrophysics Data System (ADS)

Sulaiman, Noorazliza; Mohamad-Saleh, Junita; Abro, Abdul Ghani

2015-05-01

Bio-inspired algorithms (BIAs) implemented to solve various optimization problems have shown promising results which are very important in this severely complex real-world. Artificial Bee Colony (ABC) algorithm, a kind of BIAs has demonstrated tremendous results as compared to other optimization algorithms. This paper presents a new modified ABC algorithm referred to as JA-ABC3 with the aim to enhance convergence speed and avoid premature convergence. The proposed algorithm has been simulated on ten commonly used benchmarks functions. Its performance has also been compared with other existing ABC variants. To justify its robust applicability, the proposed algorithm has been tested to solve Reactive Power Optimization problem. The results have shown that the proposed algorithm has superior performance to other existing ABC variants e.g. GABC, BABC1, BABC2, BsfABC dan IABC in terms of convergence speed. Furthermore, the proposed algorithm has also demonstrated excellence performance in solving Reactive Power Optimization problem.

Multi-Objective Ant Colony Optimization Based on the Physarum-Inspired Mathematical Model for Bi-Objective Traveling Salesman Problems

PubMed Central

Zhang, Zili; Gao, Chao; Lu, Yuxiao; Liu, Yuxin; Liang, Mingxin

2016-01-01

Bi-objective Traveling Salesman Problem (bTSP) is an important field in the operations research, its solutions can be widely applied in the real world. Many researches of Multi-objective Ant Colony Optimization (MOACOs) have been proposed to solve bTSPs. However, most of MOACOs suffer premature convergence. This paper proposes an optimization strategy for MOACOs by optimizing the initialization of pheromone matrix with the prior knowledge of Physarum-inspired Mathematical Model (PMM). PMM can find the shortest route between two nodes based on the positive feedback mechanism. The optimized algorithms, named as iPM-MOACOs, can enhance the pheromone in the short paths and promote the search ability of ants. A series of experiments are conducted and experimental results show that the proposed strategy can achieve a better compromise solution than the original MOACOs for solving bTSPs. PMID:26751562

Multi-Objective Ant Colony Optimization Based on the Physarum-Inspired Mathematical Model for Bi-Objective Traveling Salesman Problems.

PubMed

Zhang, Zili; Gao, Chao; Lu, Yuxiao; Liu, Yuxin; Liang, Mingxin

2016-01-01

Bi-objective Traveling Salesman Problem (bTSP) is an important field in the operations research, its solutions can be widely applied in the real world. Many researches of Multi-objective Ant Colony Optimization (MOACOs) have been proposed to solve bTSPs. However, most of MOACOs suffer premature convergence. This paper proposes an optimization strategy for MOACOs by optimizing the initialization of pheromone matrix with the prior knowledge of Physarum-inspired Mathematical Model (PMM). PMM can find the shortest route between two nodes based on the positive feedback mechanism. The optimized algorithms, named as iPM-MOACOs, can enhance the pheromone in the short paths and promote the search ability of ants. A series of experiments are conducted and experimental results show that the proposed strategy can achieve a better compromise solution than the original MOACOs for solving bTSPs.

Fuzzy Random λ-Mean SAD Portfolio Selection Problem: An Ant Colony Optimization Approach

NASA Astrophysics Data System (ADS)

Thakur, Gour Sundar Mitra; Bhattacharyya, Rupak; Mitra, Swapan Kumar

2010-10-01

To reach the investment goal, one has to select a combination of securities among different portfolios containing large number of securities. Only the past records of each security do not guarantee the future return. As there are many uncertain factors which directly or indirectly influence the stock market and there are also some newer stock markets which do not have enough historical data, experts' expectation and experience must be combined with the past records to generate an effective portfolio selection model. In this paper the return of security is assumed to be Fuzzy Random Variable Set (FRVS), where returns are set of random numbers which are in turn fuzzy numbers. A new λ-Mean Semi Absolute Deviation (λ-MSAD) portfolio selection model is developed. The subjective opinions of the investors to the rate of returns of each security are taken into consideration by introducing a pessimistic-optimistic parameter vector λ. λ-Mean Semi Absolute Deviation (λ-MSAD) model is preferred as it follows absolute deviation of the rate of returns of a portfolio instead of the variance as the measure of the risk. As this model can be reduced to Linear Programming Problem (LPP) it can be solved much faster than quadratic programming problems. Ant Colony Optimization (ACO) is used for solving the portfolio selection problem. ACO is a paradigm for designing meta-heuristic algorithms for combinatorial optimization problem. Data from BSE is used for illustration.

A new technique based on Artificial Bee Colony Algorithm for optimal sizing of stand-alone photovoltaic system.

PubMed

Mohamed, Ahmed F; Elarini, Mahdi M; Othman, Ahmed M

2014-05-01

One of the most recent optimization techniques applied to the optimal design of photovoltaic system to supply an isolated load demand is the Artificial Bee Colony Algorithm (ABC). The proposed methodology is applied to optimize the cost of the PV system including photovoltaic, a battery bank, a battery charger controller, and inverter. Two objective functions are proposed: the first one is the PV module output power which is to be maximized and the second one is the life cycle cost (LCC) which is to be minimized. The analysis is performed based on measured solar radiation and ambient temperature measured at Helwan city, Egypt. A comparison between ABC algorithm and Genetic Algorithm (GA) optimal results is done. Another location is selected which is Zagazig city to check the validity of ABC algorithm in any location. The ABC is more optimal than GA. The results encouraged the use of the PV systems to electrify the rural sites of Egypt.

A new technique based on Artificial Bee Colony Algorithm for optimal sizing of stand-alone photovoltaic system

PubMed Central

Mohamed, Ahmed F.; Elarini, Mahdi M.; Othman, Ahmed M.

2013-01-01

One of the most recent optimization techniques applied to the optimal design of photovoltaic system to supply an isolated load demand is the Artificial Bee Colony Algorithm (ABC). The proposed methodology is applied to optimize the cost of the PV system including photovoltaic, a battery bank, a battery charger controller, and inverter. Two objective functions are proposed: the first one is the PV module output power which is to be maximized and the second one is the life cycle cost (LCC) which is to be minimized. The analysis is performed based on measured solar radiation and ambient temperature measured at Helwan city, Egypt. A comparison between ABC algorithm and Genetic Algorithm (GA) optimal results is done. Another location is selected which is Zagazig city to check the validity of ABC algorithm in any location. The ABC is more optimal than GA. The results encouraged the use of the PV systems to electrify the rural sites of Egypt. PMID:25685507

Long-active granulocyte colony-stimulating factor for peripheral blood hematopoietic progenitor cell mobilization.

PubMed

Martino, Massimo; Laszlo, Daniele; Lanza, Francesco

2014-06-01

Peg-filgrastim (PEG-FIL), a polyethylene glycol-conjugated form of granulocyte colony-stimulating factor (G-CSF), has been introduced in clinical practice and is effective in shortening the time of neutropenia after cytotoxic chemotherapy. G-CSF has emerged as the preferred cytokine for hematopoietic progenitor cells' (HPC) mobilization. Nevertheless, data on the ability of PEG-FIL in this field have been published. We review publications in the field with the goal of providing an overview of this approach. PEG-FIL may be able to mobilize CD34(+) cells in a more timely fashion than G-CSF, with the advantages of only a single-dose administration, an earlier start and a reduction in the number of apheresis procedures. The main controversies concern the dosage of the drug and the optimal dose. In the context of chemo-mobilization, a single dose of 6 mg PEG-FIL seems effective in terms of HPC's mobilization and there is no increase in this effect if the dose is doubled to 12 mg. Steady-state mobilization requires higher doses of PEG-FIL and this approach is not cost-effective when compared with G-CSF. The experiences with PEG-FIL in the healthy donor setting are very limited.

Modelled drift patterns of fish larvae link coastal morphology to seabird colony distribution.

PubMed

Sandvik, Hanno; Barrett, Robert T; Erikstad, Kjell Einar; Myksvoll, Mari S; Vikebø, Frode; Yoccoz, Nigel G; Anker-Nilssen, Tycho; Lorentsen, Svein-Håkon; Reiertsen, Tone K; Skarðhamar, Jofrid; Skern-Mauritzen, Mette; Systad, Geir Helge

2016-05-13

Colonial breeding is an evolutionary puzzle, as the benefits of breeding in high densities are still not fully explained. Although the dynamics of existing colonies are increasingly understood, few studies have addressed the initial formation of colonies, and empirical tests are rare. Using a high-resolution larval drift model, we here document that the distribution of seabird colonies along the Norwegian coast can be explained by variations in the availability and predictability of fish larvae. The modelled variability in concentration of fish larvae is, in turn, predicted by the topography of the continental shelf and coastline. The advection of fish larvae along the coast translates small-scale topographic characteristics into a macroecological pattern, viz. the spatial distribution of top-predator breeding sites. Our findings provide empirical corroboration of the hypothesis that seabird colonies are founded in locations that minimize travel distances between breeding and foraging locations, thereby enabling optimal foraging by central-place foragers.

Improved understanding of the searching behavior of ant colony optimization algorithms applied to the water distribution design problem

NASA Astrophysics Data System (ADS)

Zecchin, A. C.; Simpson, A. R.; Maier, H. R.; Marchi, A.; Nixon, J. B.

2012-09-01

Evolutionary algorithms (EAs) have been applied successfully to many water resource problems, such as system design, management decision formulation, and model calibration. The performance of an EA with respect to a particular problem type is dependent on how effectively its internal operators balance the exploitation/exploration trade-off to iteratively find solutions of an increasing quality. For a given problem, different algorithms are observed to produce a variety of different final performances, but there have been surprisingly few investigations into characterizing how the different internal mechanisms alter the algorithm's searching behavior, in both the objective and decision space, to arrive at this final performance. This paper presents metrics for analyzing the searching behavior of ant colony optimization algorithms, a particular type of EA, for the optimal water distribution system design problem, which is a classical NP-hard problem in civil engineering. Using the proposed metrics, behavior is characterized in terms of three different attributes: (1) the effectiveness of the search in improving its solution quality and entering into optimal or near-optimal regions of the search space, (2) the extent to which the algorithm explores as it converges to solutions, and (3) the searching behavior with respect to the feasible and infeasible regions. A range of case studies is considered, where a number of ant colony optimization variants are applied to a selection of water distribution system optimization problems. The results demonstrate the utility of the proposed metrics to give greater insight into how the internal operators affect each algorithm's searching behavior.

A novel approach for dimension reduction of microarray.

PubMed

Aziz, Rabia; Verma, C K; Srivastava, Namita

2017-12-01

This paper proposes a new hybrid search technique for feature (gene) selection (FS) using Independent component analysis (ICA) and Artificial Bee Colony (ABC) called ICA+ABC, to select informative genes based on a Naïve Bayes (NB) algorithm. An important trait of this technique is the optimization of ICA feature vector using ABC. ICA+ABC is a hybrid search algorithm that combines the benefits of extraction approach, to reduce the size of data and wrapper approach, to optimize the reduced feature vectors. This hybrid search technique is facilitated by evaluating the performance of ICA+ABC on six standard gene expression datasets of classification. Extensive experiments were conducted to compare the performance of ICA+ABC with the results obtained from recently published Minimum Redundancy Maximum Relevance (mRMR) +ABC algorithm for NB classifier. Also to check the performance that how ICA+ABC works as feature selection with NB classifier, compared the combination of ICA with popular filter techniques and with other similar bio inspired algorithm such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The result shows that ICA+ABC has a significant ability to generate small subsets of genes from the ICA feature vector, that significantly improve the classification accuracy of NB classifier compared to other previously suggested methods. Copyright © 2017 Elsevier Ltd. All rights reserved.

Evolutionary Fuzzy Control and Navigation for Two Wheeled Robots Cooperatively Carrying an Object in Unknown Environments.

PubMed

Juang, Chia-Feng; Lai, Min-Ge; Zeng, Wan-Ting

2015-09-01

This paper presents a method that allows two wheeled, mobile robots to navigate unknown environments while cooperatively carrying an object. In the navigation method, a leader robot and a follower robot cooperatively perform either obstacle boundary following (OBF) or target seeking (TS) to reach a destination. The two robots are controlled by fuzzy controllers (FC) whose rules are learned through an adaptive fusion of continuous ant colony optimization and particle swarm optimization (AF-CACPSO), which avoids the time-consuming task of manually designing the controllers. The AF-CACPSO-based evolutionary fuzzy control approach is first applied to the control of a single robot to perform OBF. The learning approach is then applied to achieve cooperative OBF with two robots, where an auxiliary FC designed with the AF-CACPSO is used to control the follower robot. For cooperative TS, a rule for coordination of the two robots is developed. To navigate cooperatively, a cooperative behavior supervisor is introduced to select between cooperative OBF and cooperative TS. The performance of the AF-CACPSO is verified through comparisons with various population-based optimization algorithms for the OBF learning problem. Simulations and experiments verify the effectiveness of the approach for cooperative navigation of two robots.

Patterns of split sex ratio in ants have multiple evolutionary causes based on different within-colony conflicts

PubMed Central

Kümmerli, Rolf; Keller, Laurent

2009-01-01

Split sex ratio—a pattern where colonies within a population specialize in either male or queen production—is a widespread phenomenon in ants and other social Hymenoptera. It has often been attributed to variation in colony kin structure, which affects the degree of queen–worker conflict over optimal sex allocation. However, recent findings suggest that split sex ratio is a more diverse phenomenon, which can evolve for multiple reasons. Here, we provide an overview of the main conditions favouring split sex ratio. We show that each split sex-ratio type arises due to a different combination of factors determining colony kin structure, queen or worker control over sex ratio and the type of conflict between colony members. PMID:19457886

Improved multi-objective ant colony optimization algorithm and its application in complex reasoning

NASA Astrophysics Data System (ADS)

Wang, Xinqing; Zhao, Yang; Wang, Dong; Zhu, Huijie; Zhang, Qing

2013-09-01

The problem of fault reasoning has aroused great concern in scientific and engineering fields. However, fault investigation and reasoning of complex system is not a simple reasoning decision-making problem. It has become a typical multi-constraint and multi-objective reticulate optimization decision-making problem under many influencing factors and constraints. So far, little research has been carried out in this field. This paper transforms the fault reasoning problem of complex system into a paths-searching problem starting from known symptoms to fault causes. Three optimization objectives are considered simultaneously: maximum probability of average fault, maximum average importance, and minimum average complexity of test. Under the constraints of both known symptoms and the causal relationship among different components, a multi-objective optimization mathematical model is set up, taking minimizing cost of fault reasoning as the target function. Since the problem is non-deterministic polynomial-hard(NP-hard), a modified multi-objective ant colony algorithm is proposed, in which a reachability matrix is set up to constrain the feasible search nodes of the ants and a new pseudo-random-proportional rule and a pheromone adjustment mechinism are constructed to balance conflicts between the optimization objectives. At last, a Pareto optimal set is acquired. Evaluation functions based on validity and tendency of reasoning paths are defined to optimize noninferior set, through which the final fault causes can be identified according to decision-making demands, thus realize fault reasoning of the multi-constraint and multi-objective complex system. Reasoning results demonstrate that the improved multi-objective ant colony optimization(IMACO) can realize reasoning and locating fault positions precisely by solving the multi-objective fault diagnosis model, which provides a new method to solve the problem of multi-constraint and multi-objective fault diagnosis and reasoning of complex system.

A survey about methods dedicated to epistasis detection.

PubMed

Niel, Clément; Sinoquet, Christine; Dina, Christian; Rocheleau, Ghislain

2015-01-01

During the past decade, findings of genome-wide association studies (GWAS) improved our knowledge and understanding of disease genetics. To date, thousands of SNPs have been associated with diseases and other complex traits. Statistical analysis typically looks for association between a phenotype and a SNP taken individually via single-locus tests. However, geneticists admit this is an oversimplified approach to tackle the complexity of underlying biological mechanisms. Interaction between SNPs, namely epistasis, must be considered. Unfortunately, epistasis detection gives rise to analytic challenges since analyzing every SNP combination is at present impractical at a genome-wide scale. In this review, we will present the main strategies recently proposed to detect epistatic interactions, along with their operating principle. Some of these methods are exhaustive, such as multifactor dimensionality reduction, likelihood ratio-based tests or receiver operating characteristic curve analysis; some are non-exhaustive, such as machine learning techniques (random forests, Bayesian networks) or combinatorial optimization approaches (ant colony optimization, computational evolution system).

Optimized ISRU Propellants for Propulsion and Power Needs for Future Mars Colonization

NASA Astrophysics Data System (ADS)

Rice, Eric E.; Gustafson, Robert J.; Gramer, Daniel J.; Chiaverini, Martin J.; Teeter, Ronald R.; White, Brant C.

2003-01-01

In recent studies (Rice, 2000, 2002) conducted by ORBITEC for the NASA Institute for Advanced Concepts (NIAC), we conceptualized systems and an evolving optimized architecture for producing and utilizing Mars-based in-situ space resources utilization (ISRU) propellant combinations for future Mars colonization. The propellants are to be used to support the propulsion and power systems for ground and flight vehicles. The key aspect of the study was to show the benefits of ISRU, develop an analysis methodology, as well as provide guidance to propellant system choices in the future based upon what is known today about Mars. The study time frame included an early unmanned and manned exploration period (through 2040) and two colonization scenarios that are postulated to occur from 2040 to 2090. As part of this feasibility study, ORBITEC developed two different Mars colonization scenarios: a low case that ends with a 100-person colony (an Antarctica analogy) and a high case that ends with a 10,000-person colony (a Mars terraforming scenario). A population growth model, mission traffic model, and infrastructure model were developed for each scenario to better understand the requirements of future Mars colonies. Additionally, propellant and propulsion systems design concepts were developed. Cost models were also developed to allow comparison of the different ISRU propellant approaches. This paper summarizes the overall results of the study. ISRU proved to be a key enabler for these colonization missions. Carbon monoxide and oxygen, proved to be the most cost-effective ISRU propellant combination. The entire final reports Phase I and II) and all the details can be found at the NIAC website www.niac.usra.edu.

Model Simulation of Diurnal Vertical Migration Patterns of Different-Sized Colonies of Microcystis Employing a Particle Trajectory Approach.

PubMed

Chien, Yu Ching; Wu, Shian Chee; Chen, Wan Ching; Chou, Chih Chung

2013-04-01

Microcystis , a genus of potentially harmful cyanobacteria, is known to proliferate in stratified freshwaters due to its capability to change cell density and regulate buoyancy. In this study, a trajectory model was developed to simulate the cell density change and spatial distribution of Microcystis cells with nonuniform colony sizes. Simulations showed that larger colonies migrate to the near-surface water layer during the night to effectively capture irradiation and become heavy enough to sink during daytime. Smaller-sized colonies instead took a longer time to get to the surface. Simulation of the diurnally varying Microcystis population profile matched the observed pattern in the field when the radii of the multisized colonies were in a beta distribution. This modeling approach is able to take into account the history of cells by keeping track of their positions and properties, such as cell density and the sizes of colonies. It also serves as the basis for further developmental modeling of phytoplanktons that are forming colonies and changing buoyancy.

Swarm intelligence in bioinformatics: methods and implementations for discovering patterns of multiple sequences.

PubMed

Cui, Zhihua; Zhang, Yi

2014-02-01

As a promising and innovative research field, bioinformatics has attracted increasing attention recently. Beneath the enormous number of open problems in this field, one fundamental issue is about the accurate and efficient computational methodology that can deal with tremendous amounts of data. In this paper, we survey some applications of swarm intelligence to discover patterns of multiple sequences. To provide a deep insight, ant colony optimization, particle swarm optimization, artificial bee colony and artificial fish swarm algorithm are selected, and their applications to multiple sequence alignment and motif detecting problem are discussed.

Hybrid artificial bee colony algorithm for parameter optimization of five-parameter bidirectional reflectance distribution function model.

PubMed

Wang, Qianqian; Zhao, Jing; Gong, Yong; Hao, Qun; Peng, Zhong

2017-11-20

A hybrid artificial bee colony (ABC) algorithm inspired by the best-so-far solution and bacterial chemotaxis was introduced to optimize the parameters of the five-parameter bidirectional reflectance distribution function (BRDF) model. To verify the performance of the hybrid ABC algorithm, we measured BRDF of three kinds of samples and simulated the undetermined parameters of the five-parameter BRDF model using the hybrid ABC algorithm and the genetic algorithm, respectively. The experimental results demonstrate that the hybrid ABC algorithm outperforms the genetic algorithm in convergence speed, accuracy, and time efficiency under the same conditions.

Optimal Parameter Selection for Support Vector Machine Based on Artificial Bee Colony Algorithm: A Case Study of Grid-Connected PV System Power Prediction.

PubMed

Gao, Xiang-Ming; Yang, Shi-Feng; Pan, San-Bo

2017-01-01

Predicting the output power of photovoltaic system with nonstationarity and randomness, an output power prediction model for grid-connected PV systems is proposed based on empirical mode decomposition (EMD) and support vector machine (SVM) optimized with an artificial bee colony (ABC) algorithm. First, according to the weather forecast data sets on the prediction date, the time series data of output power on a similar day with 15-minute intervals are built. Second, the time series data of the output power are decomposed into a series of components, including some intrinsic mode components IMFn and a trend component Res, at different scales using EMD. The corresponding SVM prediction model is established for each IMF component and trend component, and the SVM model parameters are optimized with the artificial bee colony algorithm. Finally, the prediction results of each model are reconstructed, and the predicted values of the output power of the grid-connected PV system can be obtained. The prediction model is tested with actual data, and the results show that the power prediction model based on the EMD and ABC-SVM has a faster calculation speed and higher prediction accuracy than do the single SVM prediction model and the EMD-SVM prediction model without optimization.

Optimal Parameter Selection for Support Vector Machine Based on Artificial Bee Colony Algorithm: A Case Study of Grid-Connected PV System Power Prediction

PubMed Central

2017-01-01

Predicting the output power of photovoltaic system with nonstationarity and randomness, an output power prediction model for grid-connected PV systems is proposed based on empirical mode decomposition (EMD) and support vector machine (SVM) optimized with an artificial bee colony (ABC) algorithm. First, according to the weather forecast data sets on the prediction date, the time series data of output power on a similar day with 15-minute intervals are built. Second, the time series data of the output power are decomposed into a series of components, including some intrinsic mode components IMFn and a trend component Res, at different scales using EMD. The corresponding SVM prediction model is established for each IMF component and trend component, and the SVM model parameters are optimized with the artificial bee colony algorithm. Finally, the prediction results of each model are reconstructed, and the predicted values of the output power of the grid-connected PV system can be obtained. The prediction model is tested with actual data, and the results show that the power prediction model based on the EMD and ABC-SVM has a faster calculation speed and higher prediction accuracy than do the single SVM prediction model and the EMD-SVM prediction model without optimization. PMID:28912803

Genetic Interaction Mapping in Schizosaccharomyces pombe Using the Pombe Epistasis Mapper (PEM) System and a ROTOR HDA Colony Replicating Robot in a 1536 Array Format.

PubMed

Roguev, Assen; Xu, Jiewei; Krogan, Nevan

2018-02-01

This protocol describes an optimized high-throughput procedure for generating double deletion mutants in Schizosaccharomyces pombe using the colony replicating robot ROTOR HDA and the PEM (pombe epistasis mapper) system. The method is based on generating high-density colony arrays (1536 colonies per agar plate) and passaging them through a series of antidiploid and mating-type selection (ADS-MTS) and double-mutant selection (DMS) steps. Detailed program parameters for each individual replication step are provided. Using this procedure, batches of 25 or more screens can be routinely performed. © 2018 Cold Spring Harbor Laboratory Press.

Detection and Length Estimation of Linear Scratch on Solid Surfaces Using an Angle Constrained Ant Colony Technique

NASA Astrophysics Data System (ADS)

Pal, Siddharth; Basak, Aniruddha; Das, Swagatam

In many manufacturing areas the detection of surface defects is one of the most important processes in quality control. Currently in order to detect small scratches on solid surfaces most of the industries working on material manufacturing rely on visual inspection primarily. In this article we propose a hybrid computational intelligence technique to automatically detect a linear scratch from a solid surface and estimate its length (in pixel unit) simultaneously. The approach is based on a swarm intelligence algorithm called Ant Colony Optimization (ACO) and image preprocessing with Wiener and Sobel filters as well as the Canny edge detector. The ACO algorithm is mostly used to compensate for the broken parts of the scratch. Our experimental results confirm that the proposed technique can be used for detecting scratches from noisy and degraded images, even when it is very difficult for conventional image processing to distinguish the scratch area from its background.

Marking individual ants for behavioral sampling in a laboratory colony.

PubMed

Holbrook, C Tate

2009-07-01

Ant societies are tractable and malleable, two features that make them ideal models for probing the organization of complex biological systems. The ability to identify specific individuals while they function as part of a colony permits an integrative analysis of social complexity, including self-organizational processes (i.e., how individual-level properties and social interactions give rise to emergent, colony-level attributes such as division of labor and collective decision making). Effects of genotype, nutrition, and physiology on individual behavior and the organization of work also can be investigated in this manner, through correlative and manipulative approaches. Moreover, aspects of colony demography (e.g., colony size, and age and size distributions of workers) can be altered experimentally to examine colony development and regulatory mechanisms underlying colony homeostasis and resiliency. This protocol describes how to sample the behavior of ants living in a colony under laboratory conditions. Specifically, it outlines how to identify and observe individuals within a colony, an approach that can be used to quantify individual- and colony-level patterns of behavior. When a lower-resolution measure of overall group behavior is desired, individual identities might not be required. Given the diversity of ants and their study, this protocol provides a very general methodology; the details can be modified according to the body size, colony size, and ecology of the focal species, as well as to specific research aims. These basic techniques can also be extended to more advanced experimental designs such as manipulation of colony demography and hormone treatment.

A new improved artificial bee colony algorithm for ship hull form optimization

NASA Astrophysics Data System (ADS)

Huang, Fuxin; Wang, Lijue; Yang, Chi

2016-04-01

The artificial bee colony (ABC) algorithm is a relatively new swarm intelligence-based optimization algorithm. Its simplicity of implementation, relatively few parameter settings and promising optimization capability make it widely used in different fields. However, it has problems of slow convergence due to its solution search equation. Here, a new solution search equation based on a combination of the elite solution pool and the block perturbation scheme is proposed to improve the performance of the algorithm. In addition, two different solution search equations are used by employed bees and onlooker bees to balance the exploration and exploitation of the algorithm. The developed algorithm is validated by a set of well-known numerical benchmark functions. It is then applied to optimize two ship hull forms with minimum resistance. The tested results show that the proposed new improved ABC algorithm can outperform the ABC algorithm in most of the tested problems.

3D sensor placement strategy using the full-range pheromone ant colony system

NASA Astrophysics Data System (ADS)

Shuo, Feng; Jingqing, Jia

2016-07-01

An optimized sensor placement strategy will be extremely beneficial to ensure the safety and cost reduction considerations of structural health monitoring (SHM) systems. The sensors must be placed such that important dynamic information is obtained and the number of sensors is minimized. The practice is to select individual sensor directions by several 1D sensor methods and the triaxial sensors are placed in these directions for monitoring. However, this may lead to non-optimal placement of many triaxial sensors. In this paper, a new method, called FRPACS, is proposed based on the ant colony system (ACS) to solve the optimal placement of triaxial sensors. The triaxial sensors are placed as single units in an optimal fashion. And then the new method is compared with other algorithms using Dalian North Bridge. The computational precision and iteration efficiency of the FRPACS has been greatly improved compared with the original ACS and EFI method.

A new distributed systems scheduling algorithm: a swarm intelligence approach

NASA Astrophysics Data System (ADS)

Haghi Kashani, Mostafa; Sarvizadeh, Raheleh; Jameii, Mahdi

2011-12-01

The scheduling problem in distributed systems is known as an NP-complete problem, and methods based on heuristic or metaheuristic search have been proposed to obtain optimal and suboptimal solutions. The task scheduling is a key factor for distributed systems to gain better performance. In this paper, an efficient method based on memetic algorithm is developed to solve the problem of distributed systems scheduling. With regard to load balancing efficiently, Artificial Bee Colony (ABC) has been applied as local search in the proposed memetic algorithm. The proposed method has been compared to existing memetic-Based approach in which Learning Automata method has been used as local search. The results demonstrated that the proposed method outperform the above mentioned method in terms of communication cost.

Classification of Medical Datasets Using SVMs with Hybrid Evolutionary Algorithms Based on Endocrine-Based Particle Swarm Optimization and Artificial Bee Colony Algorithms.

PubMed

Lin, Kuan-Cheng; Hsieh, Yi-Hsiu

2015-10-01

The classification and analysis of data is an important issue in today's research. Selecting a suitable set of features makes it possible to classify an enormous quantity of data quickly and efficiently. Feature selection is generally viewed as a problem of feature subset selection, such as combination optimization problems. Evolutionary algorithms using random search methods have proven highly effective in obtaining solutions to problems of optimization in a diversity of applications. In this study, we developed a hybrid evolutionary algorithm based on endocrine-based particle swarm optimization (EPSO) and artificial bee colony (ABC) algorithms in conjunction with a support vector machine (SVM) for the selection of optimal feature subsets for the classification of datasets. The results of experiments using specific UCI medical datasets demonstrate that the accuracy of the proposed hybrid evolutionary algorithm is superior to that of basic PSO, EPSO and ABC algorithms, with regard to classification accuracy using subsets with a reduced number of features.

Hierarchical Artificial Bee Colony Algorithm for RFID Network Planning Optimization

PubMed Central

Ma, Lianbo; Chen, Hanning; Hu, Kunyuan; Zhu, Yunlong

2014-01-01

This paper presents a novel optimization algorithm, namely, hierarchical artificial bee colony optimization, called HABC, to tackle the radio frequency identification network planning (RNP) problem. In the proposed multilevel model, the higher-level species can be aggregated by the subpopulations from lower level. In the bottom level, each subpopulation employing the canonical ABC method searches the part-dimensional optimum in parallel, which can be constructed into a complete solution for the upper level. At the same time, the comprehensive learning method with crossover and mutation operators is applied to enhance the global search ability between species. Experiments are conducted on a set of 10 benchmark optimization problems. The results demonstrate that the proposed HABC obtains remarkable performance on most chosen benchmark functions when compared to several successful swarm intelligence and evolutionary algorithms. Then HABC is used for solving the real-world RNP problem on two instances with different scales. Simulation results show that the proposed algorithm is superior for solving RNP, in terms of optimization accuracy and computation robustness. PMID:24592200

Hierarchical artificial bee colony algorithm for RFID network planning optimization.

PubMed

Ma, Lianbo; Chen, Hanning; Hu, Kunyuan; Zhu, Yunlong

2014-01-01

This paper presents a novel optimization algorithm, namely, hierarchical artificial bee colony optimization, called HABC, to tackle the radio frequency identification network planning (RNP) problem. In the proposed multilevel model, the higher-level species can be aggregated by the subpopulations from lower level. In the bottom level, each subpopulation employing the canonical ABC method searches the part-dimensional optimum in parallel, which can be constructed into a complete solution for the upper level. At the same time, the comprehensive learning method with crossover and mutation operators is applied to enhance the global search ability between species. Experiments are conducted on a set of 10 benchmark optimization problems. The results demonstrate that the proposed HABC obtains remarkable performance on most chosen benchmark functions when compared to several successful swarm intelligence and evolutionary algorithms. Then HABC is used for solving the real-world RNP problem on two instances with different scales. Simulation results show that the proposed algorithm is superior for solving RNP, in terms of optimization accuracy and computation robustness.

A multilevel ant colony optimization algorithm for classical and isothermic DNA sequencing by hybridization with multiplicity information available.

PubMed

Kwarciak, Kamil; Radom, Marcin; Formanowicz, Piotr

2016-04-01

The classical sequencing by hybridization takes into account a binary information about sequence composition. A given element from an oligonucleotide library is or is not a part of the target sequence. However, the DNA chip technology has been developed and it enables to receive a partial information about multiplicity of each oligonucleotide the analyzed sequence consist of. Currently, it is not possible to assess the exact data of such type but even partial information should be very useful. Two realistic multiplicity information models are taken into consideration in this paper. The first one, called "one and many" assumes that it is possible to obtain information if a given oligonucleotide occurs in a reconstructed sequence once or more than once. According to the second model, called "one, two and many", one is able to receive from biochemical experiment information if a given oligonucleotide is present in an analyzed sequence once, twice or at least three times. An ant colony optimization algorithm has been implemented to verify the above models and to compare with existing algorithms for sequencing by hybridization which utilize the additional information. The proposed algorithm solves the problem with any kind of hybridization errors. Computational experiment results confirm that using even the partial information about multiplicity leads to increased quality of reconstructed sequences. Moreover, they also show that the more precise model enables to obtain better solutions and the ant colony optimization algorithm outperforms the existing ones. Test data sets and the proposed ant colony optimization algorithm are available on: http://bioserver.cs.put.poznan.pl/download/ACO4mSBH.zip. Copyright © 2016 Elsevier Ltd. All rights reserved.

Synergistic action of the benzene metabolite hydroquinone on myelopoietic stimulating activity of granulocyte/macrophage colony-stimulating factor in vitro

NASA Technical Reports Server (NTRS)

Irons, R. D.; Stillman, W. S.; Colagiovanni, D. B.; Henry, V. A.; Clarkson, T. W. (Principal Investigator)

1992-01-01

The effects of in vitro pretreatment with benzene metabolites on colony-forming response of murine bone marrow cells stimulated with recombinant granulocyte/macrophage colony-stimulating factor (rGM-CSF) were examined. Pretreatment with hydroquinone (HQ) at concentrations ranging from picomolar to micromolar for 30 min resulted in a 1.5- to 4.6-fold enhancement in colonies formed in response to rGM-CSF that was due to an increase in granulocyte/macrophage colonies. The synergism equaled or exceeded that reported for the effects of interleukin 1, interleukin 3, or interleukin 6 with GM-CSF. Optimal enhancement was obtained with 1 microM HQ and was largely independent of the concentration of rGM-CSF. Pretreatment with other authentic benzene metabolites, phenol and catechol, and the putative metabolite trans, trans-muconaldehyde did not enhance growth factor response. Coadministration of phenol and HQ did not enhance the maximal rGM-CSF response obtained with HQ alone but shifted the optimal concentration to 100 pM. Synergism between HQ and rGM-CSF was observed with nonadherent bone marrow cells and lineage-depleted bone marrow cells, suggesting an intrinsic effect on recruitment of myeloid progenitor cells not normally responsive to rGM-CSF. Alterations in differentiation in a myeloid progenitor cell population may be of relevance in the pathogenesis of acute myelogenous leukemia secondary to drug or chemical exposure.

A critical number of workers in a honeybee colony triggers investment in reproduction

NASA Astrophysics Data System (ADS)

Smith, Michael L.; Ostwald, Madeleine M.; Loftus, J. Carter; Seeley, Thomas D.

2014-10-01

Social insect colonies, like individual organisms, must decide as they develop how to allocate optimally their resources among survival, growth, and reproduction. Only when colonies reach a certain state do they switch from investing purely in survival and growth to investing also in reproduction. But how do worker bees within a colony detect that their colony has reached the state where it is adaptive to begin investing in reproduction? Previous work has shown that larger honeybee colonies invest more in reproduction (i.e., the production of drones and queens), however, the term `larger' encompasses multiple colony parameters including number of adult workers, size of the nest, amount of brood, and size of the honey stores. These colony parameters were independently increased in this study to test which one(s) would increase a colony's investment in reproduction via males. This was assayed by measuring the construction of drone comb, the special type of comb in which drones are reared. Only an increase in the number of workers stimulated construction of drone comb. Colonies with over 4,000 workers began building drone comb, independent of the other colony parameters. These results show that attaining a critical number of workers is the key parameter for honeybee colonies to start to shift resources towards reproduction. These findings are relevant to other social systems in which a group's members must adjust their behavior as a function of the group's size.

Modelled drift patterns of fish larvae link coastal morphology to seabird colony distribution

PubMed Central

Sandvik, Hanno; Barrett, Robert T.; Erikstad, Kjell Einar; Myksvoll, Mari S.; Vikebø, Frode; Yoccoz, Nigel G.; Anker-Nilssen, Tycho; Lorentsen, Svein-Håkon; Reiertsen, Tone K.; Skarðhamar, Jofrid; Skern-Mauritzen, Mette; Systad, Geir Helge

2016-01-01

Colonial breeding is an evolutionary puzzle, as the benefits of breeding in high densities are still not fully explained. Although the dynamics of existing colonies are increasingly understood, few studies have addressed the initial formation of colonies, and empirical tests are rare. Using a high-resolution larval drift model, we here document that the distribution of seabird colonies along the Norwegian coast can be explained by variations in the availability and predictability of fish larvae. The modelled variability in concentration of fish larvae is, in turn, predicted by the topography of the continental shelf and coastline. The advection of fish larvae along the coast translates small-scale topographic characteristics into a macroecological pattern, viz. the spatial distribution of top-predator breeding sites. Our findings provide empirical corroboration of the hypothesis that seabird colonies are founded in locations that minimize travel distances between breeding and foraging locations, thereby enabling optimal foraging by central-place foragers. PMID:27173005

DISSECTING COLONY DEVELOPMENT OF NEUROSPORA CRASSA USING mRNA PROFILING AND COMPARTATIVE GENOMICS APPROACHES

USDA-ARS?s Scientific Manuscript database

Colony development, which includes hyphal extension, branching, anastomosis and asexual sporulation are fundamental aspects of the lifecycle of filamentous fungi; genetic mechanisms underlying these phenomena are poorly understood. We conducted transcriptional profiling during colony development of...

Gis-Based Route Finding Using ANT Colony Optimization and Urban Traffic Data from Different Sources

NASA Astrophysics Data System (ADS)

Davoodi, M.; Mesgari, M. S.

2015-12-01

Nowadays traffic data is obtained from multiple sources including GPS, Video Vehicle Detectors (VVD), Automatic Number Plate Recognition (ANPR), Floating Car Data (FCD), VANETs, etc. All such data can be used for route finding. This paper proposes a model for finding the optimum route based on the integration of traffic data from different sources. Ant Colony Optimization is applied in this paper because the concept of this method (movement of ants in a network) is similar to urban road network and movements of cars. The results indicate that this model is capable of incorporating data from different sources, which may even be inconsistent.

Protein-ligand docking using fitness learning-based artificial bee colony with proximity stimuli.

PubMed

Uehara, Shota; Fujimoto, Kazuhiro J; Tanaka, Shigenori

2015-07-07

Protein-ligand docking is an optimization problem, which aims to identify the binding pose of a ligand with the lowest energy in the active site of a target protein. In this study, we employed a novel optimization algorithm called fitness learning-based artificial bee colony with proximity stimuli (FlABCps) for docking. Simulation results revealed that FlABCps improved the success rate of docking, compared to four state-of-the-art algorithms. The present results also showed superior docking performance of FlABCps, in particular for dealing with highly flexible ligands and proteins with a wide and shallow binding pocket.

An improved harmony search algorithm for emergency inspection scheduling

NASA Astrophysics Data System (ADS)

Kallioras, Nikos A.; Lagaros, Nikos D.; Karlaftis, Matthew G.

2014-11-01

The ability of nature-inspired search algorithms to efficiently handle combinatorial problems, and their successful implementation in many fields of engineering and applied sciences, have led to the development of new, improved algorithms. In this work, an improved harmony search (IHS) algorithm is presented, while a holistic approach for solving the problem of post-disaster infrastructure management is also proposed. The efficiency of IHS is compared with that of the algorithms of particle swarm optimization, differential evolution, basic harmony search and the pure random search procedure, when solving the districting problem that is the first part of post-disaster infrastructure management. The ant colony optimization algorithm is employed for solving the associated routing problem that constitutes the second part. The comparison is based on the quality of the results obtained, the computational demands and the sensitivity on the algorithmic parameters.

Object Detection Based on Template Matching through Use of Best-So-Far ABC

PubMed Central

2014-01-01

Best-so-far ABC is a modified version of the artificial bee colony (ABC) algorithm used for optimization tasks. This algorithm is one of the swarm intelligence (SI) algorithms proposed in recent literature, in which the results demonstrated that the best-so-far ABC can produce higher quality solutions with faster convergence than either the ordinary ABC or the current state-of-the-art ABC-based algorithm. In this work, we aim to apply the best-so-far ABC-based approach for object detection based on template matching by using the difference between the RGB level histograms corresponding to the target object and the template object as the objective function. Results confirm that the proposed method was successful in both detecting objects and optimizing the time used to reach the solution. PMID:24812556

Outplanting technique, host genotype, and site affect the initial success of outplanted Acropora cervicornis

PubMed Central

Gilliam, David S.

2018-01-01

Acropora cervicornis is the most widely used coral species for reef restoration in the greater Caribbean. However, outplanting methodologies (e.g., colony density, size, host genotype, and attachment technique) vary greatly, and to date have not been evaluated for optimality across multiple sites. Two experiments were completed during this study, the first evaluated the effects of attachment technique, colony size, and genotype by outplanting 405 A. cervicornis colonies, from ten genotypes, four size classes, and three attachment techniques (epoxy, nail and cable tie, or puck) across three sites. Colony survival, health condition, tissue productivity, and growth were assessed across one year for this experiment. The second experiment assessed the effect of colony density by outplanting colonies in plots of one, four, or 25 corals per 4 m2 across four separate sites. Plot survival and condition were evaluated across two years for this experiment in order to better capture the effect of increasing cover. Colonies attached with a nail and cable tie resulted in the highest survival regardless of colony size. Small corals had the lowest survival, but the greatest productivity. The majority of colony loss was attributed to missing colonies and was highest for pucks and small epoxied colonies. Disease and predation were observed at all sites, but did not affect all genotypes, however due to the overall low prevalence of either condition there were no significant differences found in any comparison. Low density plots had significantly higher survival and significantly lower prevalence of disease, predation, and missing colonies than high density plots. These results indicate that to increase initial outplant success, colonies of many genotypes should be outplanted to multiple sites using a nail and cable tie, in low densities, and with colonies over 15 cm total linear extension. PMID:29507829

Optic disc detection using ant colony optimization

NASA Astrophysics Data System (ADS)

Dias, Marcy A.; Monteiro, Fernando C.

2012-09-01

The retinal fundus images are used in the treatment and diagnosis of several eye diseases, such as diabetic retinopathy and glaucoma. This paper proposes a new method to detect the optic disc (OD) automatically, due to the fact that the knowledge of the OD location is essential to the automatic analysis of retinal images. Ant Colony Optimization (ACO) is an optimization algorithm inspired by the foraging behaviour of some ant species that has been applied in image processing for edge detection. Recently, the ACO was used in fundus images to detect edges, and therefore, to segment the OD and other anatomical retinal structures. We present an algorithm for the detection of OD in the retina which takes advantage of the Gabor wavelet transform, entropy and ACO algorithm. Forty images of the retina from DRIVE database were used to evaluate the performance of our method.

An individual-based modeling approach to simulate the effects of cellular nutrient competition on Escherichia coli K-12 MG1655 colony behavior and interactions in aerobic structured food systems.

PubMed

Tack, Ignace L M M; Logist, Filip; Noriega Fernández, Estefanía; Van Impe, Jan F M

2015-02-01

Traditional kinetic models in predictive microbiology reliably predict macroscopic dynamics of planktonically-growing cell cultures in homogeneous liquid food systems. However, most food products have a semi-solid structure, where microorganisms grow locally in colonies. Individual colony cells exhibit strongly different and non-normally distributed behavior due to local nutrient competition. As a result, traditional models considering average population behavior in a homogeneous system do not describe colony dynamics in full detail. To incorporate local resource competition and individual cell differences, an individual-based modeling approach has been applied to Escherichia coli K-12 MG1655 colonies, considering the microbial cell as modeling unit. The first contribution of this individual-based model is to describe single colony growth under nutrient-deprived conditions. More specifically, the linear and stationary phase in the evolution of the colony radius, the evolution from a disk-like to branching morphology, and the emergence of a starvation zone in the colony center are simulated and compared to available experimental data. These phenomena occur earlier at more severe nutrient depletion conditions, i.e., at lower nutrient diffusivity and initial nutrient concentration in the medium. Furthermore, intercolony interactions have been simulated. Higher inoculum densities lead to stronger intercolony interactions, such as colony merging and smaller colony sizes, due to nutrient competition. This individual-based model contributes to the elucidation of characteristic experimentally observed colony behavior from mechanistic information about cellular physiology and interactions. Copyright © 2014 Elsevier Ltd. All rights reserved.

A Novel Artificial Bee Colony Based Clustering Algorithm for Categorical Data

PubMed Central

2015-01-01

Data with categorical attributes are ubiquitous in the real world. However, existing partitional clustering algorithms for categorical data are prone to fall into local optima. To address this issue, in this paper we propose a novel clustering algorithm, ABC-K-Modes (Artificial Bee Colony clustering based on K-Modes), based on the traditional k-modes clustering algorithm and the artificial bee colony approach. In our approach, we first introduce a one-step k-modes procedure, and then integrate this procedure with the artificial bee colony approach to deal with categorical data. In the search process performed by scout bees, we adopt the multi-source search inspired by the idea of batch processing to accelerate the convergence of ABC-K-Modes. The performance of ABC-K-Modes is evaluated by a series of experiments in comparison with that of the other popular algorithms for categorical data. PMID:25993469

A novel artificial bee colony based clustering algorithm for categorical data.

PubMed

Ji, Jinchao; Pang, Wei; Zheng, Yanlin; Wang, Zhe; Ma, Zhiqiang

2015-01-01

Data with categorical attributes are ubiquitous in the real world. However, existing partitional clustering algorithms for categorical data are prone to fall into local optima. To address this issue, in this paper we propose a novel clustering algorithm, ABC-K-Modes (Artificial Bee Colony clustering based on K-Modes), based on the traditional k-modes clustering algorithm and the artificial bee colony approach. In our approach, we first introduce a one-step k-modes procedure, and then integrate this procedure with the artificial bee colony approach to deal with categorical data. In the search process performed by scout bees, we adopt the multi-source search inspired by the idea of batch processing to accelerate the convergence of ABC-K-Modes. The performance of ABC-K-Modes is evaluated by a series of experiments in comparison with that of the other popular algorithms for categorical data.

ABCluster: the artificial bee colony algorithm for cluster global optimization.

PubMed

Zhang, Jun; Dolg, Michael

2015-10-07

Global optimization of cluster geometries is of fundamental importance in chemistry and an interesting problem in applied mathematics. In this work, we introduce a relatively new swarm intelligence algorithm, i.e. the artificial bee colony (ABC) algorithm proposed in 2005, to this field. It is inspired by the foraging behavior of a bee colony, and only three parameters are needed to control it. We applied it to several potential functions of quite different nature, i.e., the Coulomb-Born-Mayer, Lennard-Jones, Morse, Z and Gupta potentials. The benchmarks reveal that for long-ranged potentials the ABC algorithm is very efficient in locating the global minimum, while for short-ranged ones it is sometimes trapped into a local minimum funnel on a potential energy surface of large clusters. We have released an efficient, user-friendly, and free program "ABCluster" to realize the ABC algorithm. It is a black-box program for non-experts as well as experts and might become a useful tool for chemists to study clusters.

EEG/ERP adaptive noise canceller design with controlled search space (CSS) approach in cuckoo and other optimization algorithms.

PubMed

Ahirwal, M K; Kumar, Anil; Singh, G K

2013-01-01

This paper explores the migration of adaptive filtering with swarm intelligence/evolutionary techniques employed in the field of electroencephalogram/event-related potential noise cancellation or extraction. A new approach is proposed in the form of controlled search space to stabilize the randomness of swarm intelligence techniques especially for the EEG signal. Swarm-based algorithms such as Particles Swarm Optimization, Artificial Bee Colony, and Cuckoo Optimization Algorithm with their variants are implemented to design optimized adaptive noise canceler. The proposed controlled search space technique is tested on each of the swarm intelligence techniques and is found to be more accurate and powerful. Adaptive noise canceler with traditional algorithms such as least-mean-square, normalized least-mean-square, and recursive least-mean-square algorithms are also implemented to compare the results. ERP signals such as simulated visual evoked potential, real visual evoked potential, and real sensorimotor evoked potential are used, due to their physiological importance in various EEG studies. Average computational time and shape measures of evolutionary techniques are observed 8.21E-01 sec and 1.73E-01, respectively. Though, traditional algorithms take negligible time consumption, but are unable to offer good shape preservation of ERP, noticed as average computational time and shape measure difference, 1.41E-02 sec and 2.60E+00, respectively.

Effect of Methodological and Ecological Approaches on Heterogeneity of Nest-Site Selection of a Long-Lived Vulture

PubMed Central

Moreno-Opo, Rubén; Fernández-Olalla, Mariana; Margalida, Antoni; Arredondo, Ángel; Guil, Francisco

2012-01-01

The application of scientific-based conservation measures requires that sampling methodologies in studies modelling similar ecological aspects produce comparable results making easier their interpretation. We aimed to show how the choice of different methodological and ecological approaches can affect conclusions in nest-site selection studies along different Palearctic meta-populations of an indicator species. First, a multivariate analysis of the variables affecting nest-site selection in a breeding colony of cinereous vulture (Aegypius monachus) in central Spain was performed. Then, a meta-analysis was applied to establish how methodological and habitat-type factors determine differences and similarities in the results obtained by previous studies that have modelled the forest breeding habitat of the species. Our results revealed patterns in nesting-habitat modelling by the cinereous vulture throughout its whole range: steep and south-facing slopes, great cover of large trees and distance to human activities were generally selected. The ratio and situation of the studied plots (nests/random), the use of plots vs. polygons as sampling units and the number of years of data set determined the variability explained by the model. Moreover, a greater size of the breeding colony implied that ecological and geomorphological variables at landscape level were more influential. Additionally, human activities affected in greater proportion to colonies situated in Mediterranean forests. For the first time, a meta-analysis regarding the factors determining nest-site selection heterogeneity for a single species at broad scale was achieved. It is essential to homogenize and coordinate experimental design in modelling the selection of species' ecological requirements in order to avoid that differences in results among studies would be due to methodological heterogeneity. This would optimize best conservation and management practices for habitats and species in a global context. PMID:22413023

Fidelity-Based Ant Colony Algorithm with Q-learning of Quantum System

NASA Astrophysics Data System (ADS)

Liao, Qin; Guo, Ying; Tu, Yifeng; Zhang, Hang

2018-03-01

Quantum ant colony algorithm (ACA) has potential applications in quantum information processing, such as solutions of traveling salesman problem, zero-one knapsack problem, robot route planning problem, and so on. To shorten the search time of the ACA, we suggest the fidelity-based ant colony algorithm (FACA) for the control of quantum system. Motivated by structure of the Q-learning algorithm, we demonstrate the combination of a FACA with the Q-learning algorithm and suggest the design of a fidelity-based ant colony algorithm with the Q-learning to improve the performance of the FACA in a spin-1/2 quantum system. The numeric simulation results show that the FACA with the Q-learning can efficiently avoid trapping into local optimal policies and increase the speed of convergence process of quantum system.

Fidelity-Based Ant Colony Algorithm with Q-learning of Quantum System

NASA Astrophysics Data System (ADS)

Liao, Qin; Guo, Ying; Tu, Yifeng; Zhang, Hang

2017-12-01

Quantum ant colony algorithm (ACA) has potential applications in quantum information processing, such as solutions of traveling salesman problem, zero-one knapsack problem, robot route planning problem, and so on. To shorten the search time of the ACA, we suggest the fidelity-based ant colony algorithm (FACA) for the control of quantum system. Motivated by structure of the Q-learning algorithm, we demonstrate the combination of a FACA with the Q-learning algorithm and suggest the design of a fidelity-based ant colony algorithm with the Q-learning to improve the performance of the FACA in a spin-1/2 quantum system. The numeric simulation results show that the FACA with the Q-learning can efficiently avoid trapping into local optimal policies and increase the speed of convergence process of quantum system.

Swarm Intelligence for Optimizing Hybridized Smoothing Filter in Image Edge Enhancement

NASA Astrophysics Data System (ADS)

Rao, B. Tirumala; Dehuri, S.; Dileep, M.; Vindhya, A.

In this modern era, image transmission and processing plays a major role. It would be impossible to retrieve information from satellite and medical images without the help of image processing techniques. Edge enhancement is an image processing step that enhances the edge contrast of an image or video in an attempt to improve its acutance. Edges are the representations of the discontinuities of image intensity functions. For processing these discontinuities in an image, a good edge enhancement technique is essential. The proposed work uses a new idea for edge enhancement using hybridized smoothening filters and we introduce a promising technique of obtaining best hybrid filter using swarm algorithms (Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO)) to search for an optimal sequence of filters from among a set of rather simple, representative image processing filters. This paper deals with the analysis of the swarm intelligence techniques through the combination of hybrid filters generated by these algorithms for image edge enhancement.

An Effective Hybrid Routing Algorithm in WSN: Ant Colony Optimization in combination with Hop Count Minimization.

PubMed

Jiang, Ailian; Zheng, Lihong

2018-03-29

Low cost, high reliability and easy maintenance are key criteria in the design of routing protocols for wireless sensor networks (WSNs). This paper investigates the existing ant colony optimization (ACO)-based WSN routing algorithms and the minimum hop count WSN routing algorithms by reviewing their strengths and weaknesses. We also consider the critical factors of WSNs, such as energy constraint of sensor nodes, network load balancing and dynamic network topology. Then we propose a hybrid routing algorithm that integrates ACO and a minimum hop count scheme. The proposed algorithm is able to find the optimal routing path with minimal total energy consumption and balanced energy consumption on each node. The algorithm has unique superiority in terms of searching for the optimal path, balancing the network load and the network topology maintenance. The WSN model and the proposed algorithm have been implemented using C++. Extensive simulation experimental results have shown that our algorithm outperforms several other WSN routing algorithms on such aspects that include the rate of convergence, the success rate in searching for global optimal solution, and the network lifetime.

An Effective Hybrid Routing Algorithm in WSN: Ant Colony Optimization in combination with Hop Count Minimization

PubMed Central

2018-01-01

Low cost, high reliability and easy maintenance are key criteria in the design of routing protocols for wireless sensor networks (WSNs). This paper investigates the existing ant colony optimization (ACO)-based WSN routing algorithms and the minimum hop count WSN routing algorithms by reviewing their strengths and weaknesses. We also consider the critical factors of WSNs, such as energy constraint of sensor nodes, network load balancing and dynamic network topology. Then we propose a hybrid routing algorithm that integrates ACO and a minimum hop count scheme. The proposed algorithm is able to find the optimal routing path with minimal total energy consumption and balanced energy consumption on each node. The algorithm has unique superiority in terms of searching for the optimal path, balancing the network load and the network topology maintenance. The WSN model and the proposed algorithm have been implemented using C++. Extensive simulation experimental results have shown that our algorithm outperforms several other WSN routing algorithms on such aspects that include the rate of convergence, the success rate in searching for global optimal solution, and the network lifetime. PMID:29596336

A Modified Artificial Bee Colony Algorithm Application for Economic Environmental Dispatch

NASA Astrophysics Data System (ADS)

Tarafdar Hagh, M.; Baghban Orandi, Omid

2018-03-01

In conventional fossil-fuel power systems, the economic environmental dispatch (EED) problem is a major problem that optimally determines the output power of generating units in a way that cost of total production and emission level be minimized simultaneously, and at the same time all the constraints of units and system are satisfied properly. To solve EED problem which is a non-convex optimization problem, a modified artificial bee colony (MABC) algorithm is proposed in this paper. This algorithm by implementing weighted sum method is applied on two test systems, and eventually, obtained results are compared with other reported results. Comparison of results confirms superiority and efficiency of proposed method clearly.

Ancient village fire escape path planning based on improved ant colony algorithm

NASA Astrophysics Data System (ADS)

Xia, Wei; Cao, Kang; Hu, QianChuan

2017-06-01

The roadways are narrow and perplexing in ancient villages, it brings challenges and difficulties for people to choose route to escape when a fire occurs. In this paper, a fire escape path planning method based on ant colony algorithm is presented according to the problem. The factors in the fire environment which influence the escape speed is introduced to improve the heuristic function of the algorithm, optimal transfer strategy, and adjustment pheromone volatile factor to improve pheromone update strategy adaptively, improve its dynamic search ability and search speed. Through simulation, the dynamic adjustment of the optimal escape path is obtained, and the method is proved to be feasible.

Dynamic Network Formation Using Ant Colony Optimization

DTIC Science & Technology

2009-03-01

backhauls, VRP with pick-up and delivery, VRP with satellite facilities, and VRP with time windows (Murata & Itai , 2005). The general vehicle...given route is only visited once. The objective of the basic problem is to minimize a total cost as follows (Murata & Itai , 2005): M m mc 1 min...Problem based on Ant Colony System. Second Internation Workshop on Freight Transportation and Logistics. Palermo, Italy. Murata, T., & Itai , R. (2005

Application of cellular automatons and ant algorithms in avionics

NASA Astrophysics Data System (ADS)

Kuznetsov, A. V.; Selvesiuk, N. I.; Platoshin, G. A.; Semenova, E. V.

2018-03-01

The paper considers two algorithms for searching quasi-optimal solutions of discrete optimization problems with regard to the tasks of avionics placing. The first one solves the problem of optimal placement of devices by installation locations, the second one is for the problem of finding the shortest route between devices. Solutions are constructed using a cellular automaton and the ant colony algorithm.

Power plant maintenance scheduling using ant colony optimization: an improved formulation

NASA Astrophysics Data System (ADS)

Foong, Wai Kuan; Maier, Holger; Simpson, Angus

2008-04-01

It is common practice in the hydropower industry to either shorten the maintenance duration or to postpone maintenance tasks in a hydropower system when there is expected unserved energy based on current water storage levels and forecast storage inflows. It is therefore essential that a maintenance scheduling optimizer can incorporate the options of shortening the maintenance duration and/or deferring maintenance tasks in the search for practical maintenance schedules. In this article, an improved ant colony optimization-power plant maintenance scheduling optimization (ACO-PPMSO) formulation that considers such options in the optimization process is introduced. As a result, both the optimum commencement time and the optimum outage duration are determined for each of the maintenance tasks that need to be scheduled. In addition, a local search strategy is presented in this article to boost the robustness of the algorithm. When tested on a five-station hydropower system problem, the improved formulation is shown to be capable of allowing shortening of maintenance duration in the event of expected demand shortfalls. In addition, the new local search strategy is also shown to have significantly improved the optimization ability of the ACO-PPMSO algorithm.

A Novel Method and Its Application to Measuring Pathogen Decay in Bioaerosols from Patients with Respiratory Disease

PubMed Central

Knibbs, Luke D.; Kidd, Timothy J.; Wainwright, Claire E.; Wood, Michelle E.; Ramsay, Kay A.; Bell, Scott C.; Morawska, Lidia

2016-01-01

This work aimed to develop an in vivo approach for measuring the duration of human bioaerosol infectivity. To achieve this, techniques designed to target short-term and long-term bioaerosol aging, were combined in a tandem system and optimized for the collection of human respiratory bioaerosols, without contamination. To demonstrate the technique, cough aerosols were sampled from two persons with cystic fibrosis and chronic Pseudomonas aeruginosa infection. Measurements and cultures from aerosol ages of 10, 20, 40, 900 and 2700 seconds were used to determine the optimum droplet nucleus size for pathogen transport and the airborne bacterial biological decay. The droplet nuclei containing the greatest number of colony forming bacteria per unit volume of airborne sputum were between 1.5 and 2.6 μm. Larger nuclei of 3.9 μm, were more likely to produce a colony when impacted onto growth media, because the greater volume of sputum comprising the larger droplet nuclei, compensated for lower concentrations of bacteria within the sputum of larger nuclei. Although more likely to produce a colony, the larger droplet nuclei were small in number, and the greatest numbers of colonies were instead produced by nuclei from 1.5 to 5.7 μm. Very few colonies were produced by smaller droplet nuclei, despite their very large numbers. The concentration of viable bacteria within the dried sputum comprising the droplet nuclei exhibited an orderly dual decay over time with two distinct half-lives. Nuclei exhibiting a rapid biological decay process with a 10 second half-life were quickly exhausted, leaving only a subset characterized by a half-life of greater than 10 minutes. This finding implied that a subset of bacteria present in the aerosol was resistant to rapid biological decay and remained viable in room air long enough to represent an airborne infection risk. PMID:27388489

Gas ultrasonic flow rate measurement through genetic-ant colony optimization based on the ultrasonic pulse received signal model

NASA Astrophysics Data System (ADS)

Hou, Huirang; Zheng, Dandan; Nie, Laixiao

2015-04-01

For gas ultrasonic flowmeters, the signals received by ultrasonic sensors are susceptible to noise interference. If signals are mingled with noise, a large error in flow measurement can be caused by triggering mistakenly using the traditional double-threshold method. To solve this problem, genetic-ant colony optimization (GACO) based on the ultrasonic pulse received signal model is proposed. Furthermore, in consideration of the real-time performance of the flow measurement system, the improvement of processing only the first three cycles of the received signals rather than the whole signal is proposed. Simulation results show that the GACO algorithm has the best estimation accuracy and ant-noise ability compared with the genetic algorithm, ant colony optimization, double-threshold and enveloped zero-crossing. Local convergence doesn’t appear with the GACO algorithm until -10 dB. For the GACO algorithm, the converging accuracy and converging speed and the amount of computation are further improved when using the first three cycles (called GACO-3cycles). Experimental results involving actual received signals show that the accuracy of single-gas ultrasonic flow rate measurement can reach 0.5% with GACO-3 cycles, which is better than with the double-threshold method.

New Enhanced Artificial Bee Colony (JA-ABC5) Algorithm with Application for Reactive Power Optimization

PubMed Central

2015-01-01

The standard artificial bee colony (ABC) algorithm involves exploration and exploitation processes which need to be balanced for enhanced performance. This paper proposes a new modified ABC algorithm named JA-ABC5 to enhance convergence speed and improve the ability to reach the global optimum by balancing exploration and exploitation processes. New stages have been proposed at the earlier stages of the algorithm to increase the exploitation process. Besides that, modified mutation equations have also been introduced in the employed and onlooker-bees phases to balance the two processes. The performance of JA-ABC5 has been analyzed on 27 commonly used benchmark functions and tested to optimize the reactive power optimization problem. The performance results have clearly shown that the newly proposed algorithm has outperformed other compared algorithms in terms of convergence speed and global optimum achievement. PMID:25879054

BMP2 repression and optimized culture conditions promote human bone marrow-derived mesenchymal stem cell isolation.

PubMed

Kay, Alasdair Gawain; Dale, Tina Patricia; Akram, Khondoker Mehedi; Mohan, Param; Hampson, Karen; Maffulli, Nicola; Spiteri, Monica A; El Haj, Alicia Jennifer; Forsyth, Nicholas Robert

2015-01-01

Human mesenchymal stem cells (hMSC) are multipotent progenitor cells. We propose the optimization of hMSC isolation and recovery using the application of a controlled hypoxic environment. We evaluated oxygen, glucose and serum in the recovery of hMSC from bone marrow (BMhMSC). Colony forming units-fibroblastic, cell numbers, tri-lineage differentiation, immunofluorescence and microarray were used to confirm and characterize BMhMSC. In an optimized (2% O(2), 4.5 g/l glucose and 5% serum) environment both colony forming units-fibroblastic (p = 0.01) and cell numbers (p = 0.0001) were enhanced over standard conditions. Transcriptional analysis identified differential expression of bone morphogenetic protein 2 (BMP2) and, putatively, chemokine (C-X-C motif) receptor 2 (CXCR2) signaling pathways. We have detailed a potential milestone in the process of refinement of the BMhMSC isolation process.

New enhanced artificial bee colony (JA-ABC5) algorithm with application for reactive power optimization.

PubMed

Sulaiman, Noorazliza; Mohamad-Saleh, Junita; Abro, Abdul Ghani

2015-01-01

The standard artificial bee colony (ABC) algorithm involves exploration and exploitation processes which need to be balanced for enhanced performance. This paper proposes a new modified ABC algorithm named JA-ABC5 to enhance convergence speed and improve the ability to reach the global optimum by balancing exploration and exploitation processes. New stages have been proposed at the earlier stages of the algorithm to increase the exploitation process. Besides that, modified mutation equations have also been introduced in the employed and onlooker-bees phases to balance the two processes. The performance of JA-ABC5 has been analyzed on 27 commonly used benchmark functions and tested to optimize the reactive power optimization problem. The performance results have clearly shown that the newly proposed algorithm has outperformed other compared algorithms in terms of convergence speed and global optimum achievement.

An ant colony optimization heuristic for an integrated production and distribution scheduling problem

NASA Astrophysics Data System (ADS)

Chang, Yung-Chia; Li, Vincent C.; Chiang, Chia-Ju

2014-04-01

Make-to-order or direct-order business models that require close interaction between production and distribution activities have been adopted by many enterprises in order to be competitive in demanding markets. This article considers an integrated production and distribution scheduling problem in which jobs are first processed by one of the unrelated parallel machines and then distributed to corresponding customers by capacitated vehicles without intermediate inventory. The objective is to find a joint production and distribution schedule so that the weighted sum of total weighted job delivery time and the total distribution cost is minimized. This article presents a mathematical model for describing the problem and designs an algorithm using ant colony optimization. Computational experiments illustrate that the algorithm developed is capable of generating near-optimal solutions. The computational results also demonstrate the value of integrating production and distribution in the model for the studied problem.

Annealing Ant Colony Optimization with Mutation Operator for Solving TSP.

PubMed

Mohsen, Abdulqader M

2016-01-01

Ant Colony Optimization (ACO) has been successfully applied to solve a wide range of combinatorial optimization problems such as minimum spanning tree, traveling salesman problem, and quadratic assignment problem. Basic ACO has drawbacks of trapping into local minimum and low convergence rate. Simulated annealing (SA) and mutation operator have the jumping ability and global convergence; and local search has the ability to speed up the convergence. Therefore, this paper proposed a hybrid ACO algorithm integrating the advantages of ACO, SA, mutation operator, and local search procedure to solve the traveling salesman problem. The core of algorithm is based on the ACO. SA and mutation operator were used to increase the ants population diversity from time to time and the local search was used to exploit the current search area efficiently. The comparative experiments, using 24 TSP instances from TSPLIB, show that the proposed algorithm outperformed some well-known algorithms in the literature in terms of solution quality.

Stable and accurate methods for identification of water bodies from Landsat series imagery using meta-heuristic algorithms

NASA Astrophysics Data System (ADS)

Gamshadzaei, Mohammad Hossein; Rahimzadegan, Majid

2017-10-01

Identification of water extents in Landsat images is challenging due to surfaces with similar reflectance to water extents. The objective of this study is to provide stable and accurate methods for identifying water extents in Landsat images based on meta-heuristic algorithms. Then, seven Landsat images were selected from various environmental regions in Iran. Training of the algorithms was performed using 40 water pixels and 40 nonwater pixels in operational land imager images of Chitgar Lake (one of the study regions). Moreover, high-resolution images from Google Earth were digitized to evaluate the results. Two approaches were considered: index-based and artificial intelligence (AI) algorithms. In the first approach, nine common water spectral indices were investigated. AI algorithms were utilized to acquire coefficients of optimal band combinations to extract water extents. Among the AI algorithms, the artificial neural network algorithm and also the ant colony optimization, genetic algorithm, and particle swarm optimization (PSO) meta-heuristic algorithms were implemented. Index-based methods represented different performances in various regions. Among AI methods, PSO had the best performance with average overall accuracy and kappa coefficient of 93% and 98%, respectively. The results indicated the applicability of acquired band combinations to extract accurately and stably water extents in Landsat imagery.

A modified active appearance model based on an adaptive artificial bee colony.

PubMed

Abdulameer, Mohammed Hasan; Sheikh Abdullah, Siti Norul Huda; Othman, Zulaiha Ali

2014-01-01

Active appearance model (AAM) is one of the most popular model-based approaches that have been extensively used to extract features by highly accurate modeling of human faces under various physical and environmental circumstances. However, in such active appearance model, fitting the model with original image is a challenging task. State of the art shows that optimization method is applicable to resolve this problem. However, another common problem is applying optimization. Hence, in this paper we propose an AAM based face recognition technique, which is capable of resolving the fitting problem of AAM by introducing a new adaptive ABC algorithm. The adaptation increases the efficiency of fitting as against the conventional ABC algorithm. We have used three datasets: CASIA dataset, property 2.5D face dataset, and UBIRIS v1 images dataset in our experiments. The results have revealed that the proposed face recognition technique has performed effectively, in terms of accuracy of face recognition.

Protein Tertiary Structure Prediction Based on Main Chain Angle Using a Hybrid Bees Colony Optimization Algorithm

NASA Astrophysics Data System (ADS)

Mahmood, Zakaria N.; Mahmuddin, Massudi; Mahmood, Mohammed Nooraldeen

Encoding proteins of amino acid sequence to predict classified into their respective families and subfamilies is important research area. However for a given protein, knowing the exact action whether hormonal, enzymatic, transmembranal or nuclear receptors does not depend solely on amino acid sequence but on the way the amino acid thread folds as well. This study provides a prototype system that able to predict a protein tertiary structure. Several methods are used to develop and evaluate the system to produce better accuracy in protein 3D structure prediction. The Bees Optimization algorithm which inspired from the honey bees food foraging method, is used in the searching phase. In this study, the experiment is conducted on short sequence proteins that have been used by the previous researches using well-known tools. The proposed approach shows a promising result.

Internationalization of an African University in the Post-Colonial Era: A Case Study of the University of Nairobi

ERIC Educational Resources Information Center

Otieno, Iddah Aoko

2012-01-01

This case study uses post-colonial and dependency theoretical lenses to investigate the forces influencing policy, procedures, and participation in international activity in the post-colonial African university environment of Kenya's first national public university-the University of Nairobi (UoN). The research addresses (1) the approaches and…

Indigenous Australian Women's Leadership: Stayin' Strong against the Post-Colonial Tide

ERIC Educational Resources Information Center

White, Nereda

2010-01-01

In this article, I reflect on my experiences as an Indigenous woman researcher coming to grips with colonialism through a post-colonialism lens. I also discuss a study which examines the leadership journey of a group of Indigenous Australian women. The research, which includes an auto-ethnographic approach, was guided by an Indigenous worldview…

Attractors in Sequence Space: Agent-Based Exploration of MHC I Binding Peptides.

PubMed

Jäger, Natalie; Wisniewska, Joanna M; Hiss, Jan A; Freier, Anja; Losch, Florian O; Walden, Peter; Wrede, Paul; Schneider, Gisbert

2010-01-12

Ant Colony Optimization (ACO) is a meta-heuristic that utilizes a computational analogue of ant trail pheromones to solve combinatorial optimization problems. The size of the ant colony and the representation of the ants' pheromone trails is unique referring to the given optimization problem. In the present study, we employed ACO to generate novel peptides that stabilize MHC I protein on the plasma membrane of a murine lymphoma cell line. A jury of feedforward neural network classifiers served as fitness function for peptide design by ACO. Bioactive murine MHC I H-2K(b) stabilizing as well as nonstabilizing octapeptides were designed, synthesized and tested. These peptides reveal residue motifs that are relevant for MHC I receptor binding. We demonstrate how the performance of the implemented ACO algorithm depends on the colony size and the size of the search space. The actual peptide design process by ACO constitutes a search path in sequence space that can be visualized as trajectories on a self-organizing map (SOM). By projecting the sequence space on a SOM we visualize the convergence of the different solutions that emerge during the optimization process in sequence space. The SOM representation reveals attractors in sequence space for MHC I binding peptides. The combination of ACO and SOM enables systematic peptide optimization. This technique allows for the rational design of various types of bioactive peptides with minimal experimental effort. Here, we demonstrate its successful application to the design of MHC-I binding and nonbinding peptides which exhibit substantial bioactivity in a cell-based assay. Copyright © 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Stories, skulls, and colonial collections.

PubMed

Roque, Ricardo

2011-01-01

The essay explores the hypothesis of colonial collecting processes involving the active addition of the colonial context and historical past to museum objects through the production of short stories. It examines the emergent historicity of collections through a focus on the "histories" that museum workers and colonial agents have been attaching to scientific collections of human skulls. Drawing on the notions of collection trajectory and historiographical work, it offers an alternative perspective from which to approach the creation of singular histories and individual archives for objects in collections.

Back analysis of geomechanical parameters in underground engineering using artificial bee colony.

PubMed

Zhu, Changxing; Zhao, Hongbo; Zhao, Ming

2014-01-01

Accurate geomechanical parameters are critical in tunneling excavation, design, and supporting. In this paper, a displacements back analysis based on artificial bee colony (ABC) algorithm is proposed to identify geomechanical parameters from monitored displacements. ABC was used as global optimal algorithm to search the unknown geomechanical parameters for the problem with analytical solution. To the problem without analytical solution, optimal back analysis is time-consuming, and least square support vector machine (LSSVM) was used to build the relationship between unknown geomechanical parameters and displacement and improve the efficiency of back analysis. The proposed method was applied to a tunnel with analytical solution and a tunnel without analytical solution. The results show the proposed method is feasible.

Application of ant colony Algorithm and particle swarm optimization in architectural design

NASA Astrophysics Data System (ADS)

Song, Ziyi; Wu, Yunfa; Song, Jianhua

2018-02-01

By studying the development of ant colony algorithm and particle swarm algorithm, this paper expounds the core idea of the algorithm, explores the combination of algorithm and architectural design, sums up the application rules of intelligent algorithm in architectural design, and combines the characteristics of the two algorithms, obtains the research route and realization way of intelligent algorithm in architecture design. To establish algorithm rules to assist architectural design. Taking intelligent algorithm as the beginning of architectural design research, the authors provide the theory foundation of ant colony Algorithm and particle swarm algorithm in architectural design, popularize the application range of intelligent algorithm in architectural design, and provide a new idea for the architects.

Christianisation, Frenchification and Malgachisation: Mission Education during War and Rebellion in French Colonial Madagascar in the 1940s

ERIC Educational Resources Information Center

Rosnes, Ellen Vea

2017-01-01

Education was an instrument in Christian missions' and colonial powers' civilisation projects. At the same time, education was also instrumental in fostering opposition. This article approaches perceptions of education mainly from the perspective of Norwegian Lutheran missionaries in French colonial Madagascar during the 1940s. The focus is on how…

A metagenomic survey of microbes in honey bee colony collapse disorder.

PubMed

Cox-Foster, Diana L; Conlan, Sean; Holmes, Edward C; Palacios, Gustavo; Evans, Jay D; Moran, Nancy A; Quan, Phenix-Lan; Briese, Thomas; Hornig, Mady; Geiser, David M; Martinson, Vince; vanEngelsdorp, Dennis; Kalkstein, Abby L; Drysdale, Andrew; Hui, Jeffrey; Zhai, Junhui; Cui, Liwang; Hutchison, Stephen K; Simons, Jan Fredrik; Egholm, Michael; Pettis, Jeffery S; Lipkin, W Ian

2007-10-12

In colony collapse disorder (CCD), honey bee colonies inexplicably lose their workers. CCD has resulted in a loss of 50 to 90% of colonies in beekeeping operations across the United States. The observation that irradiated combs from affected colonies can be repopulated with naive bees suggests that infection may contribute to CCD. We used an unbiased metagenomic approach to survey microflora in CCD hives, normal hives, and imported royal jelly. Candidate pathogens were screened for significance of association with CCD by the examination of samples collected from several sites over a period of 3 years. One organism, Israeli acute paralysis virus of bees, was strongly correlated with CCD.

An Energy-Efficient Approach to Enhance Virtual Sensors Provisioning in Sensor Clouds Environments

PubMed Central

Filho, Raimir Holanda; Rabêlo, Ricardo de Andrade L.; de Carvalho, Carlos Giovanni N.; Mendes, Douglas Lopes de S.; Costa, Valney da Gama

2018-01-01

Virtual sensors provisioning is a central issue for sensors cloud middleware since it is responsible for selecting physical nodes, usually from Wireless Sensor Networks (WSN) of different owners, to handle user’s queries or applications. Recent works perform provisioning by clustering sensor nodes based on the correlation measurements and then selecting as few nodes as possible to preserve WSN energy. However, such works consider only homogeneous nodes (same set of sensors). Therefore, those works are not entirely appropriate for sensor clouds, which in most cases comprises heterogeneous sensor nodes. In this paper, we propose ACxSIMv2, an approach to enhance the provisioning task by considering heterogeneous environments. Two main algorithms form ACxSIMv2. The first one, ACASIMv1, creates multi-dimensional clusters of sensor nodes, taking into account the measurements correlations instead of the physical distance between nodes like most works on literature. Then, the second algorithm, ACOSIMv2, based on an Ant Colony Optimization system, selects an optimal set of sensors nodes from to respond user’s queries while attending all parameters and preserving the overall energy consumption. Results from initial experiments show that the approach reduces significantly the sensor cloud energy consumption compared to traditional works, providing a solution to be considered in sensor cloud scenarios. PMID:29495406

An Energy-Efficient Approach to Enhance Virtual Sensors Provisioning in Sensor Clouds Environments.

PubMed

Lemos, Marcus Vinícius de S; Filho, Raimir Holanda; Rabêlo, Ricardo de Andrade L; de Carvalho, Carlos Giovanni N; Mendes, Douglas Lopes de S; Costa, Valney da Gama

2018-02-26

Virtual sensors provisioning is a central issue for sensors cloud middleware since it is responsible for selecting physical nodes, usually from Wireless Sensor Networks (WSN) of different owners, to handle user's queries or applications. Recent works perform provisioning by clustering sensor nodes based on the correlation measurements and then selecting as few nodes as possible to preserve WSN energy. However, such works consider only homogeneous nodes (same set of sensors). Therefore, those works are not entirely appropriate for sensor clouds, which in most cases comprises heterogeneous sensor nodes. In this paper, we propose ACxSIMv2, an approach to enhance the provisioning task by considering heterogeneous environments. Two main algorithms form ACxSIMv2. The first one, ACASIMv1, creates multi-dimensional clusters of sensor nodes, taking into account the measurements correlations instead of the physical distance between nodes like most works on literature. Then, the second algorithm, ACOSIMv2, based on an Ant Colony Optimization system, selects an optimal set of sensors nodes from to respond user's queries while attending all parameters and preserving the overall energy consumption. Results from initial experiments show that the approach reduces significantly the sensor cloud energy consumption compared to traditional works, providing a solution to be considered in sensor cloud scenarios.

A hybrid artificial bee colony algorithm for numerical function optimization

NASA Astrophysics Data System (ADS)

Alqattan, Zakaria N.; Abdullah, Rosni

2015-02-01

Artificial Bee Colony (ABC) algorithm is one of the swarm intelligence algorithms; it has been introduced by Karaboga in 2005. It is a meta-heuristic optimization search algorithm inspired from the intelligent foraging behavior of the honey bees in nature. Its unique search process made it as one of the most competitive algorithm with some other search algorithms in the area of optimization, such as Genetic algorithm (GA) and Particle Swarm Optimization (PSO). However, the ABC performance of the local search process and the bee movement or the solution improvement equation still has some weaknesses. The ABC is good in avoiding trapping at the local optimum but it spends its time searching around unpromising random selected solutions. Inspired by the PSO, we propose a Hybrid Particle-movement ABC algorithm called HPABC, which adapts the particle movement process to improve the exploration of the original ABC algorithm. Numerical benchmark functions were used in order to experimentally test the HPABC algorithm. The results illustrate that the HPABC algorithm can outperform the ABC algorithm in most of the experiments (75% better in accuracy and over 3 times faster).

"Book Learning" versus "Adapted Education": The Impact of Phelps-Stokesism on Colonial Education Systems in Central Africa in the Interwar Period

ERIC Educational Resources Information Center

Kuster, Sybille

2007-01-01

The article discusses the dissemination and reception of a practice-oriented, community-centred approach to colonial education, which was derived from educational concepts developed for African-Americans in the United States around the turn of the twentieth century. This approach--which came to be known as "Phelps-Stokesism"--gained wide…

Reinforcement interval type-2 fuzzy controller design by online rule generation and q-value-aided ant colony optimization.

PubMed

Juang, Chia-Feng; Hsu, Chia-Hung

2009-12-01

This paper proposes a new reinforcement-learning method using online rule generation and Q-value-aided ant colony optimization (ORGQACO) for fuzzy controller design. The fuzzy controller is based on an interval type-2 fuzzy system (IT2FS). The antecedent part in the designed IT2FS uses interval type-2 fuzzy sets to improve controller robustness to noise. There are initially no fuzzy rules in the IT2FS. The ORGQACO concurrently designs both the structure and parameters of an IT2FS. We propose an online interval type-2 rule generation method for the evolution of system structure and flexible partitioning of the input space. Consequent part parameters in an IT2FS are designed using Q -values and the reinforcement local-global ant colony optimization algorithm. This algorithm selects the consequent part from a set of candidate actions according to ant pheromone trails and Q-values, both of which are updated using reinforcement signals. The ORGQACO design method is applied to the following three control problems: 1) truck-backing control; 2) magnetic-levitation control; and 3) chaotic-system control. The ORGQACO is compared with other reinforcement-learning methods to verify its efficiency and effectiveness. Comparisons with type-1 fuzzy systems verify the noise robustness property of using an IT2FS.

Adaptive cockroach swarm algorithm

NASA Astrophysics Data System (ADS)

Obagbuwa, Ibidun C.; Abidoye, Ademola P.

2017-07-01

An adaptive cockroach swarm optimization (ACSO) algorithm is proposed in this paper to strengthen the existing cockroach swarm optimization (CSO) algorithm. The ruthless component of CSO algorithm is modified by the employment of blend crossover predator-prey evolution method which helps algorithm prevent any possible population collapse, maintain population diversity and create adaptive search in each iteration. The performance of the proposed algorithm on 16 global optimization benchmark function problems was evaluated and compared with the existing CSO, cuckoo search, differential evolution, particle swarm optimization and artificial bee colony algorithms.

Indian Education in Colonial Peru

ERIC Educational Resources Information Center

Martin, Luis

1973-01-01

The Spaniards who dedicated themselves to the education of the American Indians in colonial Peru were firm believers in intellectual capabilities of the Indians and in the need to approach them through their own Quechua language. (FF)

Production of White Colonies on CHROMagar Candida Medium by Members of the Candida glabrata Clade and Other Species with Overlapping Phenotypic Traits▿

PubMed Central

Bishop, Justin A.; Chase, Nancy; Lee, Richard; Kurtzman, Cletus P.; Merz, William G.

2008-01-01

We hypothesized that species of the Candida glabrata clade and species with phenotypic traits that overlap those of C. glabrata would produce white colonies on CHROMagar Candida medium. Of 154 isolates (seven species) tested, C. bracarensis, C. nivariensis, C. norvegensis, C. glabrata, and C. inconspicua produced white colonies; the Pichia fermentans group and C. krusei did not. Many of these species are difficult to identify phenotypically; white colonies may signal the need for the use of molecular approaches. PMID:18685009

What is “colonial” about medieval colonial medicine? Iberian health in global context

PubMed Central

McCleery, Iona

2015-01-01

Colonial medicine is a thriving field of study in the history of nineteenth- and twentieth-century medicine. Medicine can be used as a lens to view colonialism in action and as a way to critique colonialism. This article argues that key debates and ideas from that modern field can fruitfully be applied to the Middle Ages, especially for the early empires of Spain and Portugal (mid-fourteenth to mid-sixteenth centuries). The article identifies key modern debates, explores approaches to colonization and colonialism in the Middle Ages and discusses how medieval and modern medicine and healthcare could be compared using colonial and postcolonial discourses. The article ends with three case studies of healthcare encounters in Madeira, Granada and Hispaniola at the end of the fifteenth century. PMID:26550030

Towards a systems approach for understanding honeybee decline: a stocktaking and synthesis of existing models

PubMed Central

Becher, Matthias A; Osborne, Juliet L; Thorbek, Pernille; Kennedy, Peter J; Grimm, Volker

2013-01-01

The health of managed and wild honeybee colonies appears to have declined substantially in Europe and the United States over the last decade. Sustainability of honeybee colonies is important not only for honey production, but also for pollination of crops and wild plants alongside other insect pollinators. A combination of causal factors, including parasites, pathogens, land use changes and pesticide usage, are cited as responsible for the increased colony mortality. However, despite detailed knowledge of the behaviour of honeybees and their colonies, there are no suitable tools to explore the resilience mechanisms of this complex system under stress. Empirically testing all combinations of stressors in a systematic fashion is not feasible. We therefore suggest a cross-level systems approach, based on mechanistic modelling, to investigate the impacts of (and interactions between) colony and land management. We review existing honeybee models that are relevant to examining the effects of different stressors on colony growth and survival. Most of these models describe honeybee colony dynamics, foraging behaviour or honeybee – varroa mite – virus interactions. We found that many, but not all, processes within honeybee colonies, epidemiology and foraging are well understood and described in the models, but there is no model that couples in-hive dynamics and pathology with foraging dynamics in realistic landscapes. Synthesis and applications. We describe how a new integrated model could be built to simulate multifactorial impacts on the honeybee colony system, using building blocks from the reviewed models. The development of such a tool would not only highlight empirical research priorities but also provide an important forecasting tool for policy makers and beekeepers, and we list examples of relevant applications to bee disease and landscape management decisions. PMID:24223431

Towards a systems approach for understanding honeybee decline: a stocktaking and synthesis of existing models.

PubMed

Becher, Matthias A; Osborne, Juliet L; Thorbek, Pernille; Kennedy, Peter J; Grimm, Volker

2013-08-01

The health of managed and wild honeybee colonies appears to have declined substantially in Europe and the United States over the last decade. Sustainability of honeybee colonies is important not only for honey production, but also for pollination of crops and wild plants alongside other insect pollinators. A combination of causal factors, including parasites, pathogens, land use changes and pesticide usage, are cited as responsible for the increased colony mortality.However, despite detailed knowledge of the behaviour of honeybees and their colonies, there are no suitable tools to explore the resilience mechanisms of this complex system under stress. Empirically testing all combinations of stressors in a systematic fashion is not feasible. We therefore suggest a cross-level systems approach, based on mechanistic modelling, to investigate the impacts of (and interactions between) colony and land management.We review existing honeybee models that are relevant to examining the effects of different stressors on colony growth and survival. Most of these models describe honeybee colony dynamics, foraging behaviour or honeybee - varroa mite - virus interactions.We found that many, but not all, processes within honeybee colonies, epidemiology and foraging are well understood and described in the models, but there is no model that couples in-hive dynamics and pathology with foraging dynamics in realistic landscapes. Synthesis and applications . We describe how a new integrated model could be built to simulate multifactorial impacts on the honeybee colony system, using building blocks from the reviewed models. The development of such a tool would not only highlight empirical research priorities but also provide an important forecasting tool for policy makers and beekeepers, and we list examples of relevant applications to bee disease and landscape management decisions.

GpIIb/IIIa+ subpopulation of rat megakaryocyte progenitor cells exhibits high responsiveness to human thrombopoietin.

PubMed

Kato, T; Horie, K; Hagiwara, T; Maeda, E; Tsumura, H; Ohashi, H; Miyazaki, H

1996-08-01

The recently cloned factor thrombopoietin (TPO) has been shown to exhibit megakaryocyte colony-stimulating activity in vitro. In this investigation, to further evaluate the action of TPO on megakaryocyte progenitor cells (colony-forming units-megakaryocyte [CFU-MK]), GpIIb/IIIa+ and GpIIb/IIIa- populations of CFU-MK were prepared from rat bone marrow cells based on their reactivity with P55 antibody, a monoclonal antibody against rat GpIIb/IIIa, and their responsiveness to recombinant human TPO (rhTPO) and recombinant rat interleukin-3 (rrIL-3) was examined using a megakaryocyte colony-forming assay (Meg-CSA). rhTPO supported only megakaryocyte colony growth from both fractions in a dose-dependent fashion. The mean colony size observed with the GpIIb/IIIa+ population was smaller than that seen with the GpIIb/IIIa- population. With the optimal concentration of either rhTPO or rrIL-3, similar numbers of megakaryocyte colonies were formed from the GpIIb/IIIa+ population previously shown to be highly enriched for CFU-MK. In contrast, the maximum number of megakaryocyte colonies from the GpIIb/IIIa- population stimulated by rhTPO was only 24.2% of that achieved with rrIL-3. Morphologic analysis of rhTPO-promoted megakaryocyte colonies from the GpIIb/IIIa+ population showed that the average colony size was smaller but that the mean diameter of individual megakaryocytes was larger than in megakaryocyte colonies promoted with rrIL-3. rhTPO plus rrIL-3, each at suboptimal concentrations, had an additive effect on proliferation of CFU-MK in the GpIIb/IIIa+ fraction, whereas rhTPO plus murine IL-6 or murine granulocyte-macrophage colony-stimulating factor (mG-M-CSF) modestly but significantly reduced megakaryocyte colony growth. These results indicate that TPO preferentially acts on GpIIb/IIIa+ late CFU-MK with lower proliferative capacity and interacts with some other cytokines in CFU-MK development.

Optimisation of a honeybee-colony's energetics via social learning based on queuing delays

NASA Astrophysics Data System (ADS)

Thenius, Ronald; Schmickl, Thomas; Crailsheim, Karl

2008-06-01

Natural selection shaped the foraging-related processes of honeybees in such a way that a colony can react to changing environmental conditions optimally. To investigate this complex dynamic social system, we developed a multi-agent model of the nectar flow inside and outside of a honeybee colony. In a honeybee colony, a temporal caste collects nectar in the environment. These foragers bring their harvest into the colony, where they unload their nectar loads to one or more storer bees. Our model predicts that a cohort of foragers, collecting nectar from a single nectar source, is able to detect changes in quality in other food sources they have never visited, via the nectar processing system of the colony. We identified two novel pathways of forager-to-forager communication. Foragers can gain information about changes in the nectar flow in the environment via changes in their mean waiting time for unloadings and the number of experienced multiple unloadings. This way two distinct groups of foragers that forage on different nectar sources and that never communicate directly can share information via a third cohort of worker bees. We show that this noisy and loosely knotted social network allows a colony to perform collective information processing, so that a single forager has all necessary information available to be able to 'tune' its social behaviour, like dancing or dance-following. This way the net nectar gain of the colony is increased.

Natal and breeding philopatry in a black brant, Branta bernicla nigricans, metapopulation

USGS Publications Warehouse

Lindberg, Mark S.; Sedinger, James S.; Derksen, Dirk V.; Rockwell, Robert F.

1998-01-01

We estimated natal and breeding philopatry and dispersal probabilities for a metapopulation of Black Brant (Branta bernicla nigricans) based on observations of marked birds at six breeding colonies in Alaska, 1986–1994. Both adult females and males exhibited high (>0.90) probability of philopatry to breeding colonies. Probability of natal philopatry was significantly higher for females than males. Natal dispersal of males was recorded between every pair of colonies, whereas natal dispersal of females was observed between only half of the colony pairs. We suggest that female-biased philopatry was the result of timing of pair formation and characteristics of the mating system of brant, rather than factors related to inbreeding avoidance or optimal discrepancy. Probability of natal philopatry of females increased with age but declined with year of banding. Age-related increase in natal philopatry was positively related to higher breeding probability of older females. Declines in natal philopatry with year of banding corresponded negatively to a period of increasing population density; therefore, local population density may influence the probability of nonbreeding and gene flow among colonies.

Annealing Ant Colony Optimization with Mutation Operator for Solving TSP

PubMed Central

2016-01-01

Ant Colony Optimization (ACO) has been successfully applied to solve a wide range of combinatorial optimization problems such as minimum spanning tree, traveling salesman problem, and quadratic assignment problem. Basic ACO has drawbacks of trapping into local minimum and low convergence rate. Simulated annealing (SA) and mutation operator have the jumping ability and global convergence; and local search has the ability to speed up the convergence. Therefore, this paper proposed a hybrid ACO algorithm integrating the advantages of ACO, SA, mutation operator, and local search procedure to solve the traveling salesman problem. The core of algorithm is based on the ACO. SA and mutation operator were used to increase the ants population diversity from time to time and the local search was used to exploit the current search area efficiently. The comparative experiments, using 24 TSP instances from TSPLIB, show that the proposed algorithm outperformed some well-known algorithms in the literature in terms of solution quality. PMID:27999590

Identifying bacterial predictors of honey bee health.

PubMed

Budge, Giles E; Adams, Ian; Thwaites, Richard; Pietravalle, Stéphane; Drew, Georgia C; Hurst, Gregory D D; Tomkies, Victoria; Boonham, Neil; Brown, Mike

2016-11-01

Non-targeted approaches are useful tools to identify new or emerging issues in bee health. Here, we utilise next generation sequencing to highlight bacteria associated with healthy and unhealthy honey bee colonies, and then use targeted methods to screen a wider pool of colonies with known health status. Our results provide the first evidence that bacteria from the genus Arsenophonus are associated with poor health in honey bee colonies. We also discovered Lactobacillus and Leuconostoc spp. were associated with healthier honey bee colonies. Our results highlight the importance of understanding how the wider microbial population relates to honey bee colony health. Crown Copyright © 2016. Published by Elsevier Inc. All rights reserved.

Design of efficient circularly symmetric two-dimensional variable digital FIR filters.

PubMed

Bindima, Thayyil; Elias, Elizabeth

2016-05-01

Circularly symmetric two-dimensional (2D) finite impulse response (FIR) filters find extensive use in image and medical applications, especially for isotropic filtering. Moreover, the design and implementation of 2D digital filters with variable fractional delay and variable magnitude responses without redesigning the filter has become a crucial topic of interest due to its significance in low-cost applications. Recently the design using fixed word length coefficients has gained importance due to the replacement of multipliers by shifters and adders, which reduces the hardware complexity. Among the various approaches to 2D design, transforming a one-dimensional (1D) filter to 2D by transformation, is reported to be an efficient technique. In this paper, 1D variable digital filters (VDFs) with tunable cut-off frequencies are designed using Farrow structure based interpolation approach, and the sub-filter coefficients in the Farrow structure are made multiplier-less using canonic signed digit (CSD) representation. The resulting performance degradation in the filters is overcome by using artificial bee colony (ABC) optimization. Finally, the optimized 1D VDFs are mapped to 2D using generalized McClellan transformation resulting in low complexity, circularly symmetric 2D VDFs with real-time tunability.

Extracting archaeal populations from iron oxidizing systems

NASA Astrophysics Data System (ADS)

Whitmore, L. M.; Hutchison, J.; Chrisler, W.; Jay, Z.; Moran, J.; Inskeep, W.; Kreuzer, H.

2013-12-01

Unique environments in Yellowstone National Park offer exceptional conditions for studying microorganisms in extreme and constrained systems. However, samples from some extreme systems often contain inorganic components that pose complications during microbial and molecular analysis. Several archaeal species are found in acidic, geothermal ferric-oxyhydroxide mats; these species have been shown to adhere to mineral surfaces in flocculated colonies. For optimal microbial analysis, (microscopy, flow cytometry, genomic extractions, proteomic analysis, stable isotope analysis, and others), improved techniques are needed to better facilitate cell detachment and separation from mineral surfaces. As a requirement, these techniques must preserve cell structure while simultaneously minimizing organic carryover to downstream analysis. Several methods have been developed for removing sediments from mixed prokaryotic populations, including ultra-centrifugation, nycodenz gradient, sucrose cushions, and cell straining. In this study we conduct a comparative analysis of mechanisms used to detach archaeal cell populations from the mineral interface. Specifically, we evaluated mechanical and chemical approaches for cell separation and homogenization. Methods were compared using confocal microscopy, flow cytometry analyses, and real-time PCR detection. The methodology and approaches identified will be used to optimize biomass collection from environmental specimens or isolates grown with solid phases.

Design of efficient circularly symmetric two-dimensional variable digital FIR filters

PubMed Central

Bindima, Thayyil; Elias, Elizabeth

2016-01-01

Circularly symmetric two-dimensional (2D) finite impulse response (FIR) filters find extensive use in image and medical applications, especially for isotropic filtering. Moreover, the design and implementation of 2D digital filters with variable fractional delay and variable magnitude responses without redesigning the filter has become a crucial topic of interest due to its significance in low-cost applications. Recently the design using fixed word length coefficients has gained importance due to the replacement of multipliers by shifters and adders, which reduces the hardware complexity. Among the various approaches to 2D design, transforming a one-dimensional (1D) filter to 2D by transformation, is reported to be an efficient technique. In this paper, 1D variable digital filters (VDFs) with tunable cut-off frequencies are designed using Farrow structure based interpolation approach, and the sub-filter coefficients in the Farrow structure are made multiplier-less using canonic signed digit (CSD) representation. The resulting performance degradation in the filters is overcome by using artificial bee colony (ABC) optimization. Finally, the optimized 1D VDFs are mapped to 2D using generalized McClellan transformation resulting in low complexity, circularly symmetric 2D VDFs with real-time tunability. PMID:27222739

Optimal Irrigation and Debridement of Infected Joint Implants

PubMed Central

Schwechter, Evan M.; Folk, David; Varshney, Avanish K.; Fries, Bettina C.; Kim, Sun Jin; Hirsh, David M.

2014-01-01

Acute postoperative and acute, late hematogenous prosthetic joint infections have been treated with 1-stage irrigation and debridement with polyethylene exchange. Success rates, however, are highly variable. Reported studies demonstrate that detergents are effective at decreasing bacterial colony counts on orthopedic implants. Our hypothesis is that the combination of a detergent and an antiseptic would be more effective than using a detergent alone to decrease colony counts from a methicillin-resistant Staphylococcus aureus biofilm-coated titanium alloy disk simulating an orthopedic implant. In our study of various agents tested, chlorhexidine gluconate scrub (antiseptic and detergent) was the most effective at decreasing bacterial colony counts both prereincubation and postreincubation of the disks; pulse lavage and scrubbing were not more effective than pulse lavage alone. PMID:21641757

Outsourcing and scheduling for a two-machine flow shop with release times

NASA Astrophysics Data System (ADS)

Ahmadizar, Fardin; Amiri, Zeinab

2018-03-01

This article addresses a two-machine flow shop scheduling problem where jobs are released intermittently and outsourcing is allowed. The first operations of outsourced jobs are processed by the first subcontractor, they are transported in batches to the second subcontractor for processing their second operations, and finally they are transported back to the manufacturer. The objective is to select a subset of jobs to be outsourced, to schedule both the in-house and the outsourced jobs, and to determine a transportation plan for the outsourced jobs so as to minimize the sum of the makespan and the outsourcing and transportation costs. Two mathematical models of the problem and several necessary optimality conditions are presented. A solution approach is then proposed by incorporating the dominance properties with an ant colony algorithm. Finally, computational experiments are conducted to evaluate the performance of the models and solution approach.

An American termite in Paris: temporal colony dynamics.

PubMed

Baudouin, Guillaume; Dedeine, Franck; Bech, Nicolas; Bankhead-Dronnet, Stéphanie; Dupont, Simon; Bagnères, Anne-Geneviève

2017-12-01

Termites of the genus Reticulitermes are widespread invaders, particularly in urban habitats. Their cryptic and subterranean lifestyle makes them difficult to detect, and we know little about their colony dynamics over time. In this study we examined the persistence of Reticulitermes flavipes (Kollar) colonies in the city of Paris over a period of 15 years. The aim was (1) to define the boundaries of colonies sampled within the same four areas over two sampling periods, (2) to determine whether the colonies identified during the first sampling period persisted to the second sampling period, and (3) to compare the results obtained when colonies were delineated using a standard population genetic approach versus a Bayesian clustering method that combined both spatial and genetic information. Herein, colony delineations were inferred from genetic differences at nine microsatellite loci and one mitochondrial locus. Four of the 18 identified colonies did not show significant differences in their genotype distributions between the two sampling periods. While allelic richness was low, making it hard to reliably distinguish colony family type, most colonies appeared to retain the same breeding structure over time. These large and expansive colonies showed an important ability to fuse (39% were mixed-family colonies), contained hundreds of reproductives and displayed evidence of isolation-by-distance, suggesting budding dispersal. These traits, which favor colony persistence over time, present a challenge for pest control efforts, which apply treatment locally. The other colonies showed significant differences, but we cannot exclude the possibility that their genotype distributions simply changed over time.

Multiple types of data are required to identify the mechanisms influencing the spatial expansion of melanoma cell colonies.

PubMed

Treloar, Katrina K; Simpson, Matthew J; Haridas, Parvathi; Manton, Kerry J; Leavesley, David I; McElwain, D L Sean; Baker, Ruth E

2013-12-12

The expansion of cell colonies is driven by a delicate balance of several mechanisms including cell motility, cell-to-cell adhesion and cell proliferation. New approaches that can be used to independently identify and quantify the role of each mechanism will help us understand how each mechanism contributes to the expansion process. Standard mathematical modelling approaches to describe such cell colony expansion typically neglect cell-to-cell adhesion, despite the fact that cell-to-cell adhesion is thought to play an important role. We use a combined experimental and mathematical modelling approach to determine the cell diffusivity, D, cell-to-cell adhesion strength, q, and cell proliferation rate, λ, in an expanding colony of MM127 melanoma cells. Using a circular barrier assay, we extract several types of experimental data and use a mathematical model to independently estimate D, q and λ. In our first set of experiments, we suppress cell proliferation and analyse three different types of data to estimate D and q. We find that standard types of data, such as the area enclosed by the leading edge of the expanding colony and more detailed cell density profiles throughout the expanding colony, does not provide sufficient information to uniquely identify D and q. We find that additional data relating to the degree of cell-to-cell clustering is required to provide independent estimates of q, and in turn D. In our second set of experiments, where proliferation is not suppressed, we use data describing temporal changes in cell density to determine the cell proliferation rate. In summary, we find that our experiments are best described using the range D=161-243μm2 hour-1, q=0.3-0.5 (low to moderate strength) and λ=0.0305-0.0398 hour-1, and with these parameters we can accurately predict the temporal variations in the spatial extent and cell density profile throughout the expanding melanoma cell colony. Our systematic approach to identify the cell diffusivity, cell-to-cell adhesion strength and cell proliferation rate highlights the importance of integrating multiple types of data to accurately quantify the factors influencing the spatial expansion of melanoma cell colonies.

Hydrodynamics of bacterial colonies: A model

NASA Astrophysics Data System (ADS)

Lega, J.; Passot, T.

2003-03-01

We propose a hydrodynamic model for the evolution of bacterial colonies growing on soft agar plates. This model consists of reaction-diffusion equations for the concentrations of nutrients, water, and bacteria, coupled to a single hydrodynamic equation for the velocity field of the bacteria-water mixture. It captures the dynamics inside the colony as well as on its boundary and allows us to identify a mechanism for collective motion towards fresh nutrients, which, in its modeling aspects, is similar to classical chemotaxis. As shown in numerical simulations, our model reproduces both usual colony shapes and typical hydrodynamic motions, such as the whirls and jets recently observed in wet colonies of Bacillus subtilis. The approach presented here could be extended to different experimental situations and provides a general framework for the use of advection-reaction-diffusion equations in modeling bacterial colonies.

Training Spiking Neural Models Using Artificial Bee Colony

PubMed Central

Vazquez, Roberto A.; Garro, Beatriz A.

2015-01-01

Spiking neurons are models designed to simulate, in a realistic manner, the behavior of biological neurons. Recently, it has been proven that this type of neurons can be applied to solve pattern recognition problems with great efficiency. However, the lack of learning strategies for training these models do not allow to use them in several pattern recognition problems. On the other hand, several bioinspired algorithms have been proposed in the last years for solving a broad range of optimization problems, including those related to the field of artificial neural networks (ANNs). Artificial bee colony (ABC) is a novel algorithm based on the behavior of bees in the task of exploring their environment to find a food source. In this paper, we describe how the ABC algorithm can be used as a learning strategy to train a spiking neuron aiming to solve pattern recognition problems. Finally, the proposed approach is tested on several pattern recognition problems. It is important to remark that to realize the powerfulness of this type of model only one neuron will be used. In addition, we analyze how the performance of these models is improved using this kind of learning strategy. PMID:25709644

Reproduction and caste ratios under stress in trematode colonies with a division of labour.

PubMed

Lloyd, Melanie M; Poulin, Robert

2013-06-01

Trematodes form clonal colonies in their first intermediate host. Individuals are, depending on species, rediae or sporocysts (which asexually reproduce) and cercariae (which develop within rediae or sporocysts and infect the next host). Some species use a division of labour within colonies, with 2 distinct redial morphs: small rediae (non-reproducing) and large rediae (individuals which produce cercariae). The theory of optimal caste ratio predicts that the ratio of caste members (small to large rediae) responds to environmental variability. This was tested in Philophthalmus sp. colonies exposed to host starvation and competition with the trematode, Maritrema novaezealandensis. Philophthalmus sp. infected snails, with and without M. novaezealandensis, were subjected to food treatments. Reproductive output, number of rediae, and the ratio of small to large rediae were compared among treatments. Philophthalmus sp. colonies responded to host starvation and competition; reproductive output was higher in well-fed snails of both infection types compared with snails in lower food treatments and well-fed, single infected snails compared with well-fed double infected snails. Furthermore, the caste ratio in Philophthalmus sp. colonies was altered in response to competition. This is the first study showing caste ratio responses to environmental pressures in trematodes with a division of labour.

Nutrient chemotaxis suppression of a diffusive instability in bacterial colony dynamics

NASA Astrophysics Data System (ADS)

Arouh, Scott; Levine, Herbert

2000-07-01

Bacteria grown on a semisolid agar surface have been observed to form branching patterns as the colony envelope propagates outward. The fundamental cause of this instability relates to the need for limited nutrient to diffuse towards the colony. Here, we investigate the effect on this instability of allowing the bacteria to move chemotactically in response to the nutrient gradient. Our results show that this additional effect has a tendency to suppress the instability. Our calculations are done within the context of a simple ``cutoff'' model of colony dynamics, but presumably remain valid for more complex and hence more realistic approaches.

Efficient distribution of toy products using ant colony optimization algorithm

NASA Astrophysics Data System (ADS)

Hidayat, S.; Nurpraja, C. A.

2017-12-01

CV Atham Toys (CVAT) produces wooden toys and furniture, comprises 13 small and medium industries. CVAT always attempt to deliver customer orders on time but delivery costs are high. This is because of inadequate infrastructure such that delivery routes are long, car maintenance costs are high, while fuel subsidy by the government is still temporary. This study seeks to minimize the cost of product distribution based on the shortest route using one of five Ant Colony Optimization (ACO) algorithms to solve the Vehicle Routing Problem (VRP). This study concludes that the best of the five is the Ant Colony System (ACS) algorithm. The best route in 1st week gave a total distance of 124.11 km at a cost of Rp 66,703.75. The 2nd week route gave a total distance of 132.27 km at a cost of Rp 71,095.13. The 3rd week best route gave a total distance of 122.70 km with a cost of Rp 65,951.25. While the 4th week gave a total distance of 132.27 km at a cost of Rp 74,083.63. Prior to this study there was no effort to calculate these figures.

Technological approaches to optimize colonial resistance control for humans in artificial environment

NASA Astrophysics Data System (ADS)

Viacheslav, Ilyin; Skedina, Marina; Muokhamedieva, Lana; Gegenava, Anna; Mardanov, Robert

Infectious safety of humans in confined habitat is one of the most important problems of contem-porary space medicine. It is known that together with increasing of space station exploration increases the risc of nosocomial-like strains formation. Investigations analyzing spaceflights on spaceships Spollo, Soyuz, Saljut, Mir revealed shifts in content of human microflora, decreas-ing of protective microflora and immunity, translocation of conventional pathogens, mainly, spaphylococci, to different biotopes. At present time, control on microbial state of cosmo-nauts is performed on Earth before and after the flight, and once in flight 5 days prior to landing. This seems to me not enough. Together with increasing of spaceflight duration it starts to be mostly actual to develop contemporary technological approaches to perform op-erative control on colonial resistance of cosmonauts. It is preferable that these means and measures should be simple and biologically safe, i.e. non-cultivating. One of such technologies is express-diagnostics of human disbiotic shifts with the aid of automatised method of digital treatment of microscopy microbial images. At present the standardized swab and automatised recognizing of microbial cells with calculation of quantitative rate of different microbial groups in tested materials and it's transformation via telecommunication channels. Knowing content and quantity of microbes in tested biotope, one can forecast risk of infection development and give countermeasures recommendation. Other prospective technology -gaseous chromatomass spectrometry which basing on analysis of different microbial volatile lipid acids can determine quantity and content of microbes. These markers are unique for different microbial specia and allow to isolate them from plenty of bioobjects. This technology was also successfully tested for space crewmembers in groundbase studies and in spaceflight. The data revealed increasing of pathogenicity potential on microflora in spaceflight, which was comparable to data obtained by classic bacteriology methods. The testings results allow to recommend this technology for colonial resistance control for humans in artificial environment.

Population Census of a Large Common Tern Colony with a Small Unmanned Aircraft

PubMed Central

Chabot, Dominique; Craik, Shawn R.; Bird, David M.

2015-01-01

Small unmanned aircraft systems (UAS) may be useful for conducting high-precision, low-disturbance waterbird surveys, but limited data exist on their effectiveness. We evaluated the capacity of a small UAS to census a large (>6,000 nests) coastal Common tern (Sterna hirundo) colony of which ground surveys are particularly disruptive and time-consuming. We compared aerial photographic tern counts to ground nest counts in 45 plots (5-m radius) throughout the colony at three intervals over a nine-day period in order to identify sources of variation and establish a coefficient to estimate nest numbers from UAS surveys. We also compared a full colony ground count to full counts from two UAS surveys conducted the following day. Finally, we compared colony disturbance levels over the course of UAS flights to matched control periods. Linear regressions between aerial and ground counts in plots had very strong correlations in all three comparison periods (R 2 = 0.972–0.989, P < 0.001) and regression coefficients ranged from 0.928–0.977 terns/nest. Full colony aerial counts were 93.6% and 94.0%, respectively, of the ground count. Varying visibility of terns with ground cover, weather conditions and image quality, and changing nest attendance rates throughout incubation were likely sources of variation in aerial detection rates. Optimally timed UAS surveys of Common tern colonies following our method should yield population estimates in the 93–96% range of ground counts. Although the terns were initially disturbed by the UAS flying overhead, they rapidly habituated to it. Overall, we found no evidence of sustained disturbance to the colony by the UAS. We encourage colonial waterbird researchers and managers to consider taking advantage of this burgeoning technology. PMID:25874997

Design of two-channel filter bank using nature inspired optimization based fractional derivative constraints.

PubMed

Kuldeep, B; Singh, V K; Kumar, A; Singh, G K

2015-01-01

In this article, a novel approach for 2-channel linear phase quadrature mirror filter (QMF) bank design based on a hybrid of gradient based optimization and optimization of fractional derivative constraints is introduced. For the purpose of this work, recently proposed nature inspired optimization techniques such as cuckoo search (CS), modified cuckoo search (MCS) and wind driven optimization (WDO) are explored for the design of QMF bank. 2-Channel QMF is also designed with particle swarm optimization (PSO) and artificial bee colony (ABC) nature inspired optimization techniques. The design problem is formulated in frequency domain as sum of L2 norm of error in passband, stopband and transition band at quadrature frequency. The contribution of this work is the novel hybrid combination of gradient based optimization (Lagrange multiplier method) and nature inspired optimization (CS, MCS, WDO, PSO and ABC) and its usage for optimizing the design problem. Performance of the proposed method is evaluated by passband error (ϕp), stopband error (ϕs), transition band error (ϕt), peak reconstruction error (PRE), stopband attenuation (As) and computational time. The design examples illustrate the ingenuity of the proposed method. Results are also compared with the other existing algorithms, and it was found that the proposed method gives best result in terms of peak reconstruction error and transition band error while it is comparable in terms of passband and stopband error. Results show that the proposed method is successful for both lower and higher order 2-channel QMF bank design. A comparative study of various nature inspired optimization techniques is also presented, and the study singles out CS as a best QMF optimization technique. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Evaluation of the selection methods used in the exIWO algorithm based on the optimization of multidimensional functions

NASA Astrophysics Data System (ADS)

Kostrzewa, Daniel; Josiński, Henryk

2016-06-01

The expanded Invasive Weed Optimization algorithm (exIWO) is an optimization metaheuristic modelled on the original IWO version inspired by dynamic growth of weeds colony. The authors of the present paper have modified the exIWO algorithm introducing a set of both deterministic and non-deterministic strategies of individuals' selection. The goal of the project was to evaluate the modified exIWO by testing its usefulness for multidimensional numerical functions optimization. The optimized functions: Griewank, Rastrigin, and Rosenbrock are frequently used as benchmarks because of their characteristics.

Seven-spot ladybird optimization: a novel and efficient metaheuristic algorithm for numerical optimization.

PubMed

Wang, Peng; Zhu, Zhouquan; Huang, Shuai

2013-01-01

This paper presents a novel biologically inspired metaheuristic algorithm called seven-spot ladybird optimization (SLO). The SLO is inspired by recent discoveries on the foraging behavior of a seven-spot ladybird. In this paper, the performance of the SLO is compared with that of the genetic algorithm, particle swarm optimization, and artificial bee colony algorithms by using five numerical benchmark functions with multimodality. The results show that SLO has the ability to find the best solution with a comparatively small population size and is suitable for solving optimization problems with lower dimensions.

Seven-Spot Ladybird Optimization: A Novel and Efficient Metaheuristic Algorithm for Numerical Optimization

PubMed Central

Zhu, Zhouquan

2013-01-01

This paper presents a novel biologically inspired metaheuristic algorithm called seven-spot ladybird optimization (SLO). The SLO is inspired by recent discoveries on the foraging behavior of a seven-spot ladybird. In this paper, the performance of the SLO is compared with that of the genetic algorithm, particle swarm optimization, and artificial bee colony algorithms by using five numerical benchmark functions with multimodality. The results show that SLO has the ability to find the best solution with a comparatively small population size and is suitable for solving optimization problems with lower dimensions. PMID:24385879

Mixed Integer Programming and Heuristic Scheduling for Space Communication Networks

NASA Technical Reports Server (NTRS)

Lee, Charles H.; Cheung, Kar-Ming

2012-01-01

In this paper, we propose to solve the constrained optimization problem in two phases. The first phase uses heuristic methods such as the ant colony method, particle swarming optimization, and genetic algorithm to seek a near optimal solution among a list of feasible initial populations. The final optimal solution can be found by using the solution of the first phase as the initial condition to the SQP algorithm. We demonstrate the above problem formulation and optimization schemes with a large-scale network that includes the DSN ground stations and a number of spacecraft of deep space missions.

Protein folding optimization based on 3D off-lattice model via an improved artificial bee colony algorithm.

PubMed

Li, Bai; Lin, Mu; Liu, Qiao; Li, Ya; Zhou, Changjun

2015-10-01

Protein folding is a fundamental topic in molecular biology. Conventional experimental techniques for protein structure identification or protein folding recognition require strict laboratory requirements and heavy operating burdens, which have largely limited their applications. Alternatively, computer-aided techniques have been developed to optimize protein structures or to predict the protein folding process. In this paper, we utilize a 3D off-lattice model to describe the original protein folding scheme as a simplified energy-optimal numerical problem, where all types of amino acid residues are binarized into hydrophobic and hydrophilic ones. We apply a balance-evolution artificial bee colony (BE-ABC) algorithm as the minimization solver, which is featured by the adaptive adjustment of search intensity to cater for the varying needs during the entire optimization process. In this work, we establish a benchmark case set with 13 real protein sequences from the Protein Data Bank database and evaluate the convergence performance of BE-ABC algorithm through strict comparisons with several state-of-the-art ABC variants in short-term numerical experiments. Besides that, our obtained best-so-far protein structures are compared to the ones in comprehensive previous literature. This study also provides preliminary insights into how artificial intelligence techniques can be applied to reveal the dynamics of protein folding. Graphical Abstract Protein folding optimization using 3D off-lattice model and advanced optimization techniques.

A Modified Active Appearance Model Based on an Adaptive Artificial Bee Colony

PubMed Central

Othman, Zulaiha Ali

2014-01-01

Active appearance model (AAM) is one of the most popular model-based approaches that have been extensively used to extract features by highly accurate modeling of human faces under various physical and environmental circumstances. However, in such active appearance model, fitting the model with original image is a challenging task. State of the art shows that optimization method is applicable to resolve this problem. However, another common problem is applying optimization. Hence, in this paper we propose an AAM based face recognition technique, which is capable of resolving the fitting problem of AAM by introducing a new adaptive ABC algorithm. The adaptation increases the efficiency of fitting as against the conventional ABC algorithm. We have used three datasets: CASIA dataset, property 2.5D face dataset, and UBIRIS v1 images dataset in our experiments. The results have revealed that the proposed face recognition technique has performed effectively, in terms of accuracy of face recognition. PMID:25165748

Optimal angle of polycrystalline silicon solar panels placed in a building using the ant colony optimization algorithm

NASA Astrophysics Data System (ADS)

Saouane, I.; Chaker, A.; Zaidi, B.; Shekhar, C.

2017-03-01

This paper describes the mathematical model used to determine the amount of solar radiation received on an inclined solar photovoltaic panel. The optimum slope angles for each month, season, and year have also been calculated for a solar photovoltaic panel. The optimization of the procedure to maximize the solar energy collected by the solar panel by varying the tilt angle is also presented. As a first step, the global solar radiation on the horizontal surface of a thermal photovoltaic panel during clear sky is estimated. Thereafter, the Muneer model, which provides the most accurate estimation of the total solar radiation at a given geographical point has been used to determine the optimum collector slope. Also, the Ant Colony Optimization (ACO) algorithm was applied to obtain the optimum tilt angle settings for PV collector to improve the PV collector efficiency. The results show good agreement between calculated and predicted results. Additionally, this paper presents studies carried out on the polycrystalline silicon solar panels for electrical energy generation in the city of Ghardaia. The electrical energy generation has been studied as a function of amount of irradiation received and the angle of optimum orientation of the solar panels.

Predicting Flood in Perlis Using Ant Colony Optimization

NASA Astrophysics Data System (ADS)

Nadia Sabri, Syaidatul; Saian, Rizauddin

2017-06-01

Flood forecasting is widely being studied in order to reduce the effect of flood such as loss of property, loss of life and contamination of water supply. Usually flood occurs due to continuous heavy rainfall. This study used a variant of Ant Colony Optimization (ACO) algorithm named the Ant-Miner to develop the classification prediction model to predict flood. However, since Ant-Miner only accept discrete data, while rainfall data is a time series data, a pre-processing steps is needed to discretize the rainfall data initially. This study used a technique called the Symbolic Aggregate Approximation (SAX) to convert the rainfall time series data into discrete data. As an addition, Simple K-Means algorithm was used to cluster the data produced by SAX. The findings show that the predictive accuracy of the classification prediction model is more than 80%.

Refraction law and Fermat principle: a project using the ant colony optimization algorithm for undergraduate students in physics

NASA Astrophysics Data System (ADS)

Vuong, Q. L.; Rigaut, C.; Gossuin, Y.

2018-07-01

A programming project for undergraduate students in physics is proposed in this work. Its goal is to check the Snell–Descartes law of refraction using the Fermat principle and the ant colony optimization algorithm. The project involves basic mathematics and physics and is adapted to students with basic programming skills. More advanced tools can be used (but are not mandatory) as parallelization or object-oriented programming, which makes the project also suitable for more experienced students. We propose two tests to validate the program. Our algorithm is able to find solutions which are close to the theoretical predictions. Two quantities are defined to study its convergence and the quality of the solutions. It is also shown that the choice of the values of the simulation parameters is important to efficiently obtain precise results.

An improved artificial bee colony algorithm based on balance-evolution strategy for unmanned combat aerial vehicle path planning.

PubMed

Li, Bai; Gong, Li-gang; Yang, Wen-lun

2014-01-01

Unmanned combat aerial vehicles (UCAVs) have been of great interest to military organizations throughout the world due to their outstanding capabilities to operate in dangerous or hazardous environments. UCAV path planning aims to obtain an optimal flight route with the threats and constraints in the combat field well considered. In this work, a novel artificial bee colony (ABC) algorithm improved by a balance-evolution strategy (BES) is applied in this optimization scheme. In this new algorithm, convergence information during the iteration is fully utilized to manipulate the exploration/exploitation accuracy and to pursue a balance between local exploitation and global exploration capabilities. Simulation results confirm that BE-ABC algorithm is more competent for the UCAV path planning scheme than the conventional ABC algorithm and two other state-of-the-art modified ABC algorithms.

Application of microchip CGE for the analysis of PEG-modified recombinant human granulocyte-colony stimulating factors.

PubMed

Park, Eun Ji; Lee, Kyung Soo; Lee, Kang Choon; Na, Dong Hee

2010-11-01

The purpose of this study was to evaluate the microchip CGE (MCGE) for the analysis of PEG-modified granulocyte-colony stimulating factor (PEG-G-CSF) prepared with PEG-aldehydes. The unmodified and PEG-modified G-CSFs were analyzed by Protein 80 and 230 Labchips on the Agilent 2100 Bioanalyzer. The MCGE allowed size-based separation and quantitation of PEG-G-CSF. The Protein 80 Labchip was useful for PEG-5K-G-CSF, while the Protein 230 Labchip was more suitable for PEG-20K-G-CSF. The MCGE was also used to monitor a search for optimal PEG-modification (PEGylation) conditions to produce mono-PEG-G-CSF. This study demonstrates the usefulness of MCGE for monitoring and optimizing the PEGylation of G-CSF with the advantages of speed, minimal sample consumption, and automatic quantitation.

Assessing the disturbance potential of small unoccupied aircraft systems (UAS) on gray seals (Halichoerus grypus) at breeding colonies in Nova Scotia, Canada

PubMed Central

Arona, Lauren; Dale, Julian; Heaslip, Susan G.; Hammill, Michael O.

2018-01-01

The use of small unoccupied aircraft systems (UAS) for ecological studies and wildlife population assessments is increasing. These methods can provide significant benefits in terms of costs and reductions in human risk, but little is known if UAS-based approaches cause disturbance of animals during operations. To address this knowledge gap, we conducted a series of UAS flights at gray seal breeding colonies on Hay and Saddle Islands in Nova Scotia, Canada. Using a small fixed-wing UAS, we assessed both immediate and short-term effects of surveys using sequential image analysis and between-flight seal counts in ten, 50 m2 random quadrats at each colony. Counts of adult gray seals and young-of-the-year animals between first and second flights revealed no changes in abundance in quadrats (matched pair t-test p > 0.69) and slopes approaching 1 for linear regression comparisons (r2 > 0.80). Sequential image analysis revealed no changes in orientation or posture of imaged animals. We also assessed the acoustic properties of the small UAS in relation to low ambient noise conditions using sound equivalent level (Leq) measurements with a calibrated U-MIK 1 and a 1/3 octave band soundscape approach. The results of Leq measurements indicate that small fixed-wing UAS are quiet, with most energy above 160 Hz, and that levels across 1/3 octave bands do not greatly exceed ambient acoustic measurements in a quiet field during operations at standard survey altitudes. As such, this platform is unlikely to acoustically disturb gray seals at breeding colonies during population surveys. The results of the present study indicate that the effects of small fixed-wing UAS on gray seals at breeding colonies are negligible, and that fixed-wing UAS-based approaches should be considered amongst best practices for assessing gray seal colonies. PMID:29576950

Facultative Control of Matrix Production Optimizes Competitive Fitness in Pseudomonas aeruginosa PA14 Biofilm Models

PubMed Central

Madsen, Jonas S.; Lin, Yu-Cheng; Squyres, Georgia R.; Price-Whelan, Alexa; de Santiago Torio, Ana; Song, Angela; Cornell, William C.; Sørensen, Søren J.

2015-01-01

As biofilms grow, resident cells inevitably face the challenge of resource limitation. In the opportunistic pathogen Pseudomonas aeruginosa PA14, electron acceptor availability affects matrix production and, as a result, biofilm morphogenesis. The secreted matrix polysaccharide Pel is required for pellicle formation and for colony wrinkling, two activities that promote access to O2. We examined the exploitability and evolvability of Pel production at the air-liquid interface (during pellicle formation) and on solid surfaces (during colony formation). Although Pel contributes to the developmental response to electron acceptor limitation in both biofilm formation regimes, we found variation in the exploitability of its production and necessity for competitive fitness between the two systems. The wild type showed a competitive advantage against a non-Pel-producing mutant in pellicles but no advantage in colonies. Adaptation to the pellicle environment selected for mutants with a competitive advantage against the wild type in pellicles but also caused a severe disadvantage in colonies, even in wrinkled colony centers. Evolution in the colony center produced divergent phenotypes, while adaptation to the colony edge produced mutants with clear competitive advantages against the wild type in this O2-replete niche. In general, the structurally heterogeneous colony environment promoted more diversification than the more homogeneous pellicle. These results suggest that the role of Pel in community structure formation in response to electron acceptor limitation is unique to specific biofilm models and that the facultative control of Pel production is required for PA14 to maintain optimum benefit in different types of communities. PMID:26431965

Facultative control of matrix production optimizes competitive fitness in Pseudomonas aeruginosa PA14 biofilm models.

PubMed

Madsen, Jonas S; Lin, Yu-Cheng; Squyres, Georgia R; Price-Whelan, Alexa; de Santiago Torio, Ana; Song, Angela; Cornell, William C; Sørensen, Søren J; Xavier, Joao B; Dietrich, Lars E P

2015-12-01

As biofilms grow, resident cells inevitably face the challenge of resource limitation. In the opportunistic pathogen Pseudomonas aeruginosa PA14, electron acceptor availability affects matrix production and, as a result, biofilm morphogenesis. The secreted matrix polysaccharide Pel is required for pellicle formation and for colony wrinkling, two activities that promote access to O2. We examined the exploitability and evolvability of Pel production at the air-liquid interface (during pellicle formation) and on solid surfaces (during colony formation). Although Pel contributes to the developmental response to electron acceptor limitation in both biofilm formation regimes, we found variation in the exploitability of its production and necessity for competitive fitness between the two systems. The wild type showed a competitive advantage against a non-Pel-producing mutant in pellicles but no advantage in colonies. Adaptation to the pellicle environment selected for mutants with a competitive advantage against the wild type in pellicles but also caused a severe disadvantage in colonies, even in wrinkled colony centers. Evolution in the colony center produced divergent phenotypes, while adaptation to the colony edge produced mutants with clear competitive advantages against the wild type in this O2-replete niche. In general, the structurally heterogeneous colony environment promoted more diversification than the more homogeneous pellicle. These results suggest that the role of Pel in community structure formation in response to electron acceptor limitation is unique to specific biofilm models and that the facultative control of Pel production is required for PA14 to maintain optimum benefit in different types of communities. Copyright © 2015, American Society for Microbiology. All Rights Reserved.

The Psychology of Superorganisms: Collective Decision Making by Insect Societies.

PubMed

Sasaki, Takao; Pratt, Stephen C

2018-01-07

Under the superorganism concept, insect societies are so tightly integrated that they possess features analogous to those of single organisms, including collective cognition. If so, colony function might fruitfully be studied using methods developed to understand individual animals. Here, we review research that uses psychological approaches to understand decision making by colonies. The application of neural models to collective choice shows fundamental similarities between how brains and colonies balance speed/accuracy trade-offs in decision making. Experimental analyses have explored collective rationality, cognitive capacity, and perceptual discrimination at both individual and colony levels. A major theme is the emergence of improved colony-level function from interactions among relatively less capable individuals. However, colonies also encounter performance costs due to their reliance on positive feedback, which generates consensus but can also amplify errors. Collective learning is a nascent field for the further application of psychological methods to colonies. The research strategy reviewed here shows how the superorganism concept can serve as more than an illustrative analogy.

Microscale to Manufacturing Scale-up of Cell-Free Cytokine Production—A New Approach for Shortening Protein Production Development Timelines

PubMed Central

Zawada, James F; Yin, Gang; Steiner, Alexander R; Yang, Junhao; Naresh, Alpana; Roy, Sushmita M; Gold, Daniel S; Heinsohn, Henry G; Murray, Christopher J

2011-01-01

Engineering robust protein production and purification of correctly folded biotherapeutic proteins in cell-based systems is often challenging due to the requirements for maintaining complex cellular networks for cell viability and the need to develop associated downstream processes that reproducibly yield biopharmaceutical products with high product quality. Here, we present an alternative Escherichia coli-based open cell-free synthesis (OCFS) system that is optimized for predictable high-yield protein synthesis and folding at any scale with straightforward downstream purification processes. We describe how the linear scalability of OCFS allows rapid process optimization of parameters affecting extract activation, gene sequence optimization, and redox folding conditions for disulfide bond formation at microliter scales. Efficient and predictable high-level protein production can then be achieved using batch processes in standard bioreactors. We show how a fully bioactive protein produced by OCFS from optimized frozen extract can be purified directly using a streamlined purification process that yields a biologically active cytokine, human granulocyte-macrophage colony-stimulating factor, produced at titers of 700 mg/L in 10 h. These results represent a milestone for in vitro protein synthesis, with potential for the cGMP production of disulfide-bonded biotherapeutic proteins. Biotechnol. Bioeng. 2011; 108:1570–1578. © 2011 Wiley Periodicals, Inc. PMID:21337337

Effect of Co on Discontinuous Precipitation Transformation with TCP Phase in Ni-based Alloy Containing Re

NASA Astrophysics Data System (ADS)

Shi, Qianying; An, Ning; Huo, Jiajie; Zheng, Yunrong; Feng, Qiang

2017-05-01

The effect of Co on discontinuous precipitation (DP) transformation involving the formation of topologically close-packed (TCP) phase was investigated in three Ni-Cr-Re model alloys containing different levels of Co. One typical TCP phase, σ, was generated within DP cellular colonies along the migrating grain boundaries in experimental alloys during aging treatment. As a result of the increased solubility of Re in the γ matrix and enlarged interlamellar spacing of σ precipitates inside of growing DP colonies, Co addition suppressed the formation of σ phase and associated DP colonies. This study suggests that Co could potentially serve as a microstructural stabilizer in Re-containing Ni-base superalloys, which provides an alternative method for the composition optimization of superalloys.

Aerial surveys adjusted by ground surveys to estimate area occupied by black-tailed prairie dog colonies

USGS Publications Warehouse

Sidle, John G.; Augustine, David J.; Johnson, Douglas H.; Miller, Sterling D.; Cully, Jack F.; Reading, Richard P.

2012-01-01

Aerial surveys using line-intercept methods are one approach to estimate the extent of prairie dog colonies in a large geographic area. Although black-tailed prairie dogs (Cynomys ludovicianus) construct conspicuous mounds at burrow openings, aerial observers have difficulty discriminating between areas with burrows occupied by prairie dogs (colonies) versus areas of uninhabited burrows (uninhabited colony sites). Consequently, aerial line-intercept surveys may overestimate prairie dog colony extent unless adjusted by an on-the-ground inspection of a sample of intercepts. We compared aerial line-intercept surveys conducted over 2 National Grasslands in Colorado, USA, with independent ground-mapping of known black-tailed prairie dog colonies. Aerial line-intercepts adjusted by ground surveys using a single activity category adjustment overestimated colonies by ≥94% on the Comanche National Grassland and ≥58% on the Pawnee National Grassland. We present a ground-survey technique that involves 1) visiting on the ground a subset of aerial intercepts classified as occupied colonies plus a subset of intercepts classified as uninhabited colony sites, and 2) based on these ground observations, recording the proportion of each aerial intercept that intersects a colony and the proportion that intersects an uninhabited colony site. Where line-intercept techniques are applied to aerial surveys or remotely sensed imagery, this method can provide more accurate estimates of black-tailed prairie dog abundance and trends

mRMR-ABC: A Hybrid Gene Selection Algorithm for Cancer Classification Using Microarray Gene Expression Profiling

PubMed Central

Alshamlan, Hala; Badr, Ghada; Alohali, Yousef

2015-01-01

An artificial bee colony (ABC) is a relatively recent swarm intelligence optimization approach. In this paper, we propose the first attempt at applying ABC algorithm in analyzing a microarray gene expression profile. In addition, we propose an innovative feature selection algorithm, minimum redundancy maximum relevance (mRMR), and combine it with an ABC algorithm, mRMR-ABC, to select informative genes from microarray profile. The new approach is based on a support vector machine (SVM) algorithm to measure the classification accuracy for selected genes. We evaluate the performance of the proposed mRMR-ABC algorithm by conducting extensive experiments on six binary and multiclass gene expression microarray datasets. Furthermore, we compare our proposed mRMR-ABC algorithm with previously known techniques. We reimplemented two of these techniques for the sake of a fair comparison using the same parameters. These two techniques are mRMR when combined with a genetic algorithm (mRMR-GA) and mRMR when combined with a particle swarm optimization algorithm (mRMR-PSO). The experimental results prove that the proposed mRMR-ABC algorithm achieves accurate classification performance using small number of predictive genes when tested using both datasets and compared to previously suggested methods. This shows that mRMR-ABC is a promising approach for solving gene selection and cancer classification problems. PMID:25961028

mRMR-ABC: A Hybrid Gene Selection Algorithm for Cancer Classification Using Microarray Gene Expression Profiling.

PubMed

Alshamlan, Hala; Badr, Ghada; Alohali, Yousef

2015-01-01

An artificial bee colony (ABC) is a relatively recent swarm intelligence optimization approach. In this paper, we propose the first attempt at applying ABC algorithm in analyzing a microarray gene expression profile. In addition, we propose an innovative feature selection algorithm, minimum redundancy maximum relevance (mRMR), and combine it with an ABC algorithm, mRMR-ABC, to select informative genes from microarray profile. The new approach is based on a support vector machine (SVM) algorithm to measure the classification accuracy for selected genes. We evaluate the performance of the proposed mRMR-ABC algorithm by conducting extensive experiments on six binary and multiclass gene expression microarray datasets. Furthermore, we compare our proposed mRMR-ABC algorithm with previously known techniques. We reimplemented two of these techniques for the sake of a fair comparison using the same parameters. These two techniques are mRMR when combined with a genetic algorithm (mRMR-GA) and mRMR when combined with a particle swarm optimization algorithm (mRMR-PSO). The experimental results prove that the proposed mRMR-ABC algorithm achieves accurate classification performance using small number of predictive genes when tested using both datasets and compared to previously suggested methods. This shows that mRMR-ABC is a promising approach for solving gene selection and cancer classification problems.

Application of ant colony algorithm in path planning of the data center room robot

NASA Astrophysics Data System (ADS)

Wang, Yong; Ma, Jianming; Wang, Ying

2017-05-01

According to the Internet Data Center (IDC) room patrol robot as the background, the robot in the search path of autonomous obstacle avoidance and path planning ability, worked out in advance of the robot room patrol mission. The simulation experimental results show that the improved ant colony algorithm for IDC room patrol robot obstacle avoidance planning, makes the robot along an optimal or suboptimal and safe obstacle avoidance path to reach the target point to complete the task. To prove the feasibility of the method.

Local differences in parasitism and competition shape defensive investment in a polymorphic eusocial bee.

PubMed

Segers, Francisca H I D; von Zuben, Lucas; Grüter, Christoph

2016-02-01

Many colonial animals rely for their defense on a soldier caste. Adaptive colony demography theory predicts that colonies should flexibly adjust the investment in different worker castes depending on the colony needs. For example, colonies should invest more in defensive workers (e.g., soldiers) in dangerous environments. However, evidence for this prediction has been mixed. We combined descriptive and experimental approaches to examine whether defensive investment and worker size are adjusted to local ecology in the only known bee with polymorphic workers, Tetragonisca angustula. Colonies of this species are defended by a morphologically specialized soldier caste. Our study included three populations that differed in the density of food competition and the occurrence of a parasitic robber bee. We found that colonies coexisting with robber bees had on average 43% more soldiers defending the nest entrance, while colonies facing stronger foraging competition had soldiers that were -6-7% smaller. We then experimentally relocated colonies to areas with different levels of competition. When released from intense food competition, body sizes of guards and foragers increased. After introducing chemical robber bee cues at nest entrances, we found both a short-term and a long-term up-regulation of the number of soldiers defending the colony. Active soldier numbers remained high after the experiment for a duration equivalent to 2-3 worker life spans. How information about past parasite threat is stored in the colony is currently unknown. In summary, T. angustula adjusts both the number and the body size of active soldiers to local ecological conditions. Competitor density also affects forager (or minor) size, an important colony trait with potential community ecological consequences. Our study supports adaptive colony demography theory in a eusocial bee and highlights the importance of colony threats and competition as selective forces shaping colony phenotype.

Relationship between symbiont density and photosynthetic carbon acquisition in the temperate coral Cladocora caespitosa

NASA Astrophysics Data System (ADS)

Hoogenboom, M.; Beraud, E.; Ferrier-Pagès, C.

2010-03-01

This study quantified variation in net photosynthetic carbon gain in response to natural fluctuations in symbiont density for the Mediterranean coral Cladocora caespitosa, and evaluated which density maximized photosynthetic carbon acquisition. To do this, carbon acquisition was modeled as an explicit function of symbiont density. The model was parameterized using measurements of rates of photosynthesis and respiration for small colonies with a broad range of zooxanthella concentrations. Results demonstrate that rates of net photosynthesis increase asymptotically with symbiont density, whereas rates of respiration increase linearly. In combination, these functional responses meant that colony energy acquisition decreased at both low and at very high zooxanthella densities. However, there was a wide range of symbiont densities for which net daily photosynthesis was approximately equivalent. Therefore, significant changes in symbiont density do not necessarily cause a change in autotrophic energy acquisition by the colony. Model estimates of the optimal range of cell densities corresponded well with independent observations of symbiont concentrations obtained from field and laboratory studies of healthy colonies. Overall, this study demonstrates that the seasonal fluctuations, in symbiont numbers observed in healthy colonies of the Mediterranean coral investigated, do not have a strong effect on photosynthetic energy acquisition.

Queen pheromone regulates egg production in a termite.

PubMed

Yamamoto, Yuuka; Matsuura, Kenji

2011-10-23

In social insects, resource allocation is a key factor that influences colony survival and growth. Optimal allocation to queens and brood is essential for maximum colony productivity, requiring colony members to have information on the total reproductive power in colonies. However, the mechanisms regulating egg production relative to the current labour force for brood care remain poorly known. Recently, a volatile chemical was identified as a termite queen pheromone that inhibits the differentiation of new neotenic reproductives (secondary reproductives developed from nymphs or workers) in Reticulitermes speratus. The same volatile chemical is also emitted by eggs. This queen pheromone would therefore be expected to act as an honest message of the reproductive power about queens. In this study, we examined how the queen pheromone influences the reproductive rate of queens in R. speratus. We compared the number of eggs produced by each queen between groups with and without exposure to artificial queen pheromone. Exposure to the pheromone resulted in a significant decrease in egg production in both single-queen and multiple-queen groups. This is the first report supporting the role of queen pheromones as a signal regulating colony-level egg production, using synthetically derived compounds in a termite.

Queen pheromone regulates egg production in a termite

PubMed Central

Yamamoto, Yuuka; Matsuura, Kenji

2011-01-01

In social insects, resource allocation is a key factor that influences colony survival and growth. Optimal allocation to queens and brood is essential for maximum colony productivity, requiring colony members to have information on the total reproductive power in colonies. However, the mechanisms regulating egg production relative to the current labour force for brood care remain poorly known. Recently, a volatile chemical was identified as a termite queen pheromone that inhibits the differentiation of new neotenic reproductives (secondary reproductives developed from nymphs or workers) in Reticulitermes speratus. The same volatile chemical is also emitted by eggs. This queen pheromone would therefore be expected to act as an honest message of the reproductive power about queens. In this study, we examined how the queen pheromone influences the reproductive rate of queens in R. speratus. We compared the number of eggs produced by each queen between groups with and without exposure to artificial queen pheromone. Exposure to the pheromone resulted in a significant decrease in egg production in both single-queen and multiple-queen groups. This is the first report supporting the role of queen pheromones as a signal regulating colony-level egg production, using synthetically derived compounds in a termite. PMID:21543395

Moving from Damage-Centered Research through Unsettling Reflexivity

ERIC Educational Resources Information Center

Calderon, Dolores

2016-01-01

The author revisits autoethnographic work in order to examine how she unwittingly incorporated damage-centered (Tuck 2009) research approaches that reproduce settler colonial understandings of marginalized communities. The paper examines the reproduction of settler colonial knowledge in ethnographic research by unearthing the inherent surveillance…

A star recognition method based on the Adaptive Ant Colony algorithm for star sensors.

PubMed

Quan, Wei; Fang, Jiancheng

2010-01-01

A new star recognition method based on the Adaptive Ant Colony (AAC) algorithm has been developed to increase the star recognition speed and success rate for star sensors. This method draws circles, with the center of each one being a bright star point and the radius being a special angular distance, and uses the parallel processing ability of the AAC algorithm to calculate the angular distance of any pair of star points in the circle. The angular distance of two star points in the circle is solved as the path of the AAC algorithm, and the path optimization feature of the AAC is employed to search for the optimal (shortest) path in the circle. This optimal path is used to recognize the stellar map and enhance the recognition success rate and speed. The experimental results show that when the position error is about 50″, the identification success rate of this method is 98% while the Delaunay identification method is only 94%. The identification time of this method is up to 50 ms.

Application of Chitosan-Zinc Oxide Nanoparticles for Lead Extraction From Water Samples by Combining Ant Colony Optimization with Artificial Neural Network

NASA Astrophysics Data System (ADS)

Khajeh, M.; Pourkarami, A.; Arefnejad, E.; Bohlooli, M.; Khatibi, A.; Ghaffari-Moghaddam, M.; Zareian-Jahromi, S.

2017-09-01

Chitosan-zinc oxide nanoparticles (CZPs) were developed for solid-phase extraction. Combined artificial neural network-ant colony optimization (ANN-ACO) was used for the simultaneous preconcentration and determination of lead (Pb2+) ions in water samples prior to graphite furnace atomic absorption spectrometry (GF AAS). The solution pH, mass of adsorbent CZPs, amount of 1-(2-pyridylazo)-2-naphthol (PAN), which was used as a complexing agent, eluent volume, eluent concentration, and flow rates of sample and eluent were used as input parameters of the ANN model, and the percentage of extracted Pb2+ ions was used as the output variable of the model. A multilayer perception network with a back-propagation learning algorithm was used to fit the experimental data. The optimum conditions were obtained based on the ACO. Under the optimized conditions, the limit of detection for Pb2+ ions was found to be 0.078 μg/L. This procedure was also successfully used to determine the amounts of Pb2+ ions in various natural water samples.

Research on global path planning based on ant colony optimization for AUV

NASA Astrophysics Data System (ADS)

Wang, Hong-Jian; Xiong, Wei

2009-03-01

Path planning is an important issue for autonomous underwater vehicles (AUVs) traversing an unknown environment such as a sea floor, a jungle, or the outer celestial planets. For this paper, global path planning using large-scale chart data was studied, and the principles of ant colony optimization (ACO) were applied. This paper introduced the idea of a visibility graph based on the grid workspace model. It also brought a series of pheromone updating rules for the ACO planning algorithm. The operational steps of the ACO algorithm are proposed as a model for a global path planning method for AUV. To mimic the process of smoothing a planned path, a cutting operator and an insertion-point operator were designed. Simulation results demonstrated that the ACO algorithm is suitable for global path planning. The system has many advantages, including that the operating path of the AUV can be quickly optimized, and it is shorter, safer, and smoother. The prototype system successfully demonstrated the feasibility of the concept, proving it can be applied to surveys of unstructured unmanned environments.

Plant resources and colony growth in an invasive ant: the importance of honeydew-producing Hemiptera in carbohydrate transfer across trophic levels.

PubMed

Helms, Ken R; Vinson, S Bradleigh

2008-04-01

Studies have suggested that plant-based nutritional resources are important in promoting high densities of omnivorous and invasive ants, but there have been no direct tests of the effects of these resources on colony productivity. We conducted an experiment designed to determine the relative importance of plants and honeydew-producing insects feeding on plants to the growth of colonies of the invasive ant Solenopsis invicta (Buren). We found that colonies of S. invicta grew substantially when they only had access to unlimited insect prey; however, colonies that also had access to plants colonized by honeydew-producing Hemiptera grew significantly and substantially ( approximately 50%) larger. Our experiment also showed that S. invicta was unable to acquire significant nutritional resources directly from the Hemiptera host plant but acquired them indirectly from honeydew. Honeydew alone is unlikely to be sufficient for colony growth, however, and both carbohydrates abundant in plants and proteins abundant in animals are likely to be necessary for optimal growth. Our experiment provides important insight into the effects of a common tritrophic interaction among an invasive mealybug, Antonina graminis (Maskell), an invasive host grass, Cynodon dactylon L. Pers., and S. invicta in the southeastern United States, suggesting that interactions among these species can be important in promoting extremely high population densities of S. invicta.

Rail Mounted Gantry Crane Scheduling Optimization in Railway Container Terminal Based on Hybrid Handling Mode

PubMed Central

Zhu, Xiaoning

2014-01-01

Rail mounted gantry crane (RMGC) scheduling is important in reducing makespan of handling operation and improving container handling efficiency. In this paper, we present an RMGC scheduling optimization model, whose objective is to determine an optimization handling sequence in order to minimize RMGC idle load time in handling tasks. An ant colony optimization is proposed to obtain near optimal solutions. Computational experiments on a specific railway container terminal are conducted to illustrate the proposed model and solution algorithm. The results show that the proposed method is effective in reducing the idle load time of RMGC. PMID:25538768

Weighted Global Artificial Bee Colony Algorithm Makes Gas Sensor Deployment Efficient

PubMed Central

Jiang, Ye; He, Ziqing; Li, Yanhai; Xu, Zhengyi; Wei, Jianming

2016-01-01

This paper proposes an improved artificial bee colony algorithm named Weighted Global ABC (WGABC) algorithm, which is designed to improve the convergence speed in the search stage of solution search equation. The new method not only considers the effect of global factors on the convergence speed in the search phase, but also provides the expression of global factor weights. Experiment on benchmark functions proved that the algorithm can improve the convergence speed greatly. We arrive at the gas diffusion concentration based on the theory of CFD and then simulate the gas diffusion model with the influence of buildings based on the algorithm. Simulation verified the effectiveness of the WGABC algorithm in improving the convergence speed in optimal deployment scheme of gas sensors. Finally, it is verified that the optimal deployment method based on WGABC algorithm can improve the monitoring efficiency of sensors greatly as compared with the conventional deployment methods. PMID:27322262

Morphological optimization for access to dual oxidants in biofilms

PubMed Central

Kempes, Christopher P.; Okegbe, Chinweike; Mears-Clarke, Zwoisaint; Follows, Michael J.; Dietrich, Lars E. P.

2014-01-01

A major theme driving research in biology is the relationship between form and function. In particular, a longstanding goal has been to understand how the evolution of multicellularity conferred fitness advantages. Here we show that biofilms of the bacterium Pseudomonas aeruginosa produce structures that maximize cellular reproduction. Specifically, we develop a mathematical model of resource availability and metabolic response within colony features. This analysis accurately predicts the measured distribution of two types of electron acceptors: oxygen, which is available from the atmosphere, and phenazines, redox-active antibiotics produced by the bacterium. Using this model, we demonstrate that the geometry of colony structures is optimal with respect to growth efficiency. Because our model is based on resource dynamics, we also can anticipate shifts in feature geometry based on changes to the availability of electron acceptors, including variations in the external availability of oxygen and genetic manipulation that renders the cells incapable of phenazine production. PMID:24335705

Cooperative path planning for multi-USV based on improved artificial bee colony algorithm

NASA Astrophysics Data System (ADS)

Cao, Lu; Chen, Qiwei

2018-03-01

Due to the complex constraints, more uncertain factors and critical real-time demand of path planning for multiple unmanned surface vehicle (multi-USV), an improved artificial bee colony (I-ABC) algorithm were proposed to solve the model of cooperative path planning for multi-USV. First the Voronoi diagram of battle field space is conceived to generate the optimal area of USVs paths. Then the chaotic searching algorithm is used to initialize the collection of paths, which is regard as foods of the ABC algorithm. With the limited data, the initial collection can search the optimal area of paths perfectly. Finally simulations of the multi-USV path planning under various threats have been carried out. Simulation results verify that the I-ABC algorithm can improve the diversity of nectar source and the convergence rate of algorithm. It can increase the adaptability of dynamic battlefield and unexpected threats for USV.

Ant Colony Optimization With Local Search for Dynamic Traveling Salesman Problems.

PubMed

Mavrovouniotis, Michalis; Muller, Felipe M; Yang, Shengxiang

2016-06-13

For a dynamic traveling salesman problem (DTSP), the weights (or traveling times) between two cities (or nodes) may be subject to changes. Ant colony optimization (ACO) algorithms have proved to be powerful methods to tackle such problems due to their adaptation capabilities. It has been shown that the integration of local search operators can significantly improve the performance of ACO. In this paper, a memetic ACO algorithm, where a local search operator (called unstring and string) is integrated into ACO, is proposed to address DTSPs. The best solution from ACO is passed to the local search operator, which removes and inserts cities in such a way that improves the solution quality. The proposed memetic ACO algorithm is designed to address both symmetric and asymmetric DTSPs. The experimental results show the efficiency of the proposed memetic algorithm for addressing DTSPs in comparison with other state-of-the-art algorithms.

Meta-heuristic ant colony optimization technique to forecast the amount of summer monsoon rainfall: skill comparison with Markov chain model

NASA Astrophysics Data System (ADS)

Chaudhuri, Sutapa; Goswami, Sayantika; Das, Debanjana; Middey, Anirban

2014-05-01

Forecasting summer monsoon rainfall with precision becomes crucial for the farmers to plan for harvesting in a country like India where the national economy is mostly based on regional agriculture. The forecast of monsoon rainfall based on artificial neural network is a well-researched problem. In the present study, the meta-heuristic ant colony optimization (ACO) technique is implemented to forecast the amount of summer monsoon rainfall for the next day over Kolkata (22.6°N, 88.4°E), India. The ACO technique belongs to swarm intelligence and simulates the decision-making processes of ant colony similar to other adaptive learning techniques. ACO technique takes inspiration from the foraging behaviour of some ant species. The ants deposit pheromone on the ground in order to mark a favourable path that should be followed by other members of the colony. A range of rainfall amount replicating the pheromone concentration is evaluated during the summer monsoon season. The maximum amount of rainfall during summer monsoon season (June—September) is observed to be within the range of 7.5-35 mm during the period from 1998 to 2007, which is in the range 4 category set by the India Meteorological Department (IMD). The result reveals that the accuracy in forecasting the amount of rainfall for the next day during the summer monsoon season using ACO technique is 95 % where as the forecast accuracy is 83 % with Markov chain model (MCM). The forecast through ACO and MCM are compared with other existing models and validated with IMD observations from 2008 to 2012.

Direct detection of various pathogens by loop-mediated isothermal amplification assays on bacterial culture and bacterial colony.

PubMed

Yan, Muxia; Li, Weidong; Zhou, Zhenwen; Peng, Hongxia; Luo, Ziyan; Xu, Ling

2017-01-01

In this work, loop-mediated isothermal amplification based detection assay using bacterial culture and bacterial colony for various common pathogens direct detection had been established, evaluated and further applied. A total of five species of common pathogens and nine detection targets (tlh, tdh and trh for V. Parahaemolyticus, rfbE, stx1 and stx2 for E. coli, oprI for P. aeruginosa, invA for Salmonella and hylA for L. monocytogenes) were performed on bacterial culture and bacterial colony LAMP. To evaluate and optimize this assay, a total of 116 standard strains were included. Then, for each detected targets, 20 random selected strains were applied. Results were determined through both visual observation of the changed color by naked eye and electrophoresis, which increased the accuracy of survey. The minimum adding quantity of each primer had been confirmed, and the optimal amplification was obtained under 65 °C for 45 min with 25 μl reaction volume. The detection limit of bacterial culture LAMP and PCR assay were determined to be 10 2 and 10 4 or 10 5  CFU/reaction, respectively. No false positive amplification was observed when subjecting the bacterial -LAMP assay to 116 reference strains. This was the first report of colony-LAMP and culture-LAMP assay, which had been demonstrated to be a fast, reliable, cost-effective and simple method on detection of various common pathogens. Copyright © 2016 Elsevier Ltd. All rights reserved.

The evolution of coloniality in birds in relation to food, habitat, predation, and life-history traits: a comparative analysis.

PubMed

Rolland, C; Danchin, E; de Fraipont, M

1998-06-01

Coloniality in birds has been intensively studied under the cost and benefit approach, but no general conclusion can be given concerning its evolutionary function. Here, we report on a comparative analysis carried out on 320 species of birds using the general method of comparative analysis for discrete variables and the contrast method to analyze the evolution of coloniality. Showing a mean of 23 convergences and 10 reversals, coloniality appears to be a rather labile trait. Colonial breeding appears strongly correlated with the absence of feeding territory, the aquatic habitat, and nest exposure to predators but was not correlated with changes in life-history traits (body mass and clutch size). The correlation of coloniality with the aquatic habitat is in fact explained by a strong correlation with the marine habitat. Unexpectedly, we found that the evolution toward a marine habitat in birds was contingent on coloniality and that coloniality evolved before the passage to a marine life. These results-along with the lack of transitions from the nonmarine to marine habitat in solitary species and the precedence of the loss of feeding territoriality on the passage to a marine life-contradict most of the hypotheses classically accepted to explain coloniality and suggest that we use a different framework to study this evolutionary enigma.

Disease limits populations: plague and black-tailed prairie dogs

USGS Publications Warehouse

Cully, Jack F.; Johnson, T.; Collinge, S.K.; Ray, C.

2010-01-01

Plague is an exotic vector-borne disease caused by the bacterium Yersinia pestis that causes mortality rates approaching 100% in black-tailed prairie dogs (Cynomys ludovicianus). We mapped the perimeter of the active portions of black-tailed prairie dog colonies annually between 1999 and 2005 at four prairie dog colony complexes in areas with a history of plague, as well as at two complexes that were located outside the distribution of plague at the time of mapping and had therefore never been affected by the disease. We hypothesized that the presence of plague would significantly reduce overall black-tailed prairie dog colony area, reduce the sizes of colonies on these landscapes, and increase nearest-neighbor distances between colonies. Within the region historically affected by plague, individual colonies were smaller, nearest-neighbor distances were greater, and the proportion of potential habitat occupied by active prairie dog colonies was smaller than at plague-free sites. Populations that endured plague were composed of fewer large colonies (>100 ha) than populations that were historically plague free. We suggest that these differences among sites in colony size and isolation may slow recolonization after extirpation. At the same time, greater intercolony distances may also reduce intercolony transmission of pathogens. Reduced transmission among smaller and more distant colonies may ultimately enhance long-term prairie dog population persistence in areas where plague is present.

Disease limits populations: plague and black-tailed prairie dogs.

PubMed

Cully, Jack F; Johnson, Tammi L; Collinge, Sharon K; Ray, Chris

2010-01-01

Plague is an exotic vector-borne disease caused by the bacterium Yersinia pestis that causes mortality rates approaching 100% in black-tailed prairie dogs (Cynomys ludovicianus). We mapped the perimeter of the active portions of black-tailed prairie dog colonies annually between 1999 and 2005 at four prairie dog colony complexes in areas with a history of plague, as well as at two complexes that were located outside the distribution of plague at the time of mapping and had therefore never been affected by the disease. We hypothesized that the presence of plague would significantly reduce overall black-tailed prairie dog colony area, reduce the sizes of colonies on these landscapes, and increase nearest-neighbor distances between colonies. Within the region historically affected by plague, individual colonies were smaller, nearest-neighbor distances were greater, and the proportion of potential habitat occupied by active prairie dog colonies was smaller than at plague-free sites. Populations that endured plague were composed of fewer large colonies (>100 ha) than populations that were historically plague free. We suggest that these differences among sites in colony size and isolation may slow recolonization after extirpation. At the same time, greater intercolony distances may also reduce intercolony transmission of pathogens. Reduced transmission among smaller and more distant colonies may ultimately enhance long-term prairie dog population persistence in areas where plague is present.

Disease Limits Populations: Plague and Black-Tailed Prairie Dogs

PubMed Central

Johnson, Tammi L.; Collinge, Sharon K.; Ray, Chris

2010-01-01

Abstract Plague is an exotic vector-borne disease caused by the bacterium Yersinia pestis that causes mortality rates approaching 100% in black-tailed prairie dogs (Cynomys ludovicianus). We mapped the perimeter of the active portions of black-tailed prairie dog colonies annually between 1999 and 2005 at four prairie dog colony complexes in areas with a history of plague, as well as at two complexes that were located outside the distribution of plague at the time of mapping and had therefore never been affected by the disease. We hypothesized that the presence of plague would significantly reduce overall black-tailed prairie dog colony area, reduce the sizes of colonies on these landscapes, and increase nearest-neighbor distances between colonies. Within the region historically affected by plague, individual colonies were smaller, nearest-neighbor distances were greater, and the proportion of potential habitat occupied by active prairie dog colonies was smaller than at plague-free sites. Populations that endured plague were composed of fewer large colonies (>100 ha) than populations that were historically plague free. We suggest that these differences among sites in colony size and isolation may slow recolonization after extirpation. At the same time, greater intercolony distances may also reduce intercolony transmission of pathogens. Reduced transmission among smaller and more distant colonies may ultimately enhance long-term prairie dog population persistence in areas where plague is present. PMID:20158327

'Compromise' in Echolocation Calls between Different Colonies of the Intermediate Leaf-Nosed Bat (Hipposideros larvatus).

PubMed

Chen, Yi; Liu, Qi; Su, Qianqian; Sun, Yunxiao; Peng, Xingwen; He, Xiangyang; Zhang, Libiao

2016-01-01

Each animal population has its own acoustic signature which facilitates identification, communication and reproduction. The sonar signals of bats can convey social information, such as species identity and contextual information. The goal of this study was to determine whether bats adjust their echolocation call structures to mutually recognize and communicate when they encounter the bats from different colonies. We used the intermediate leaf-nosed bats (Hipposideros larvatus) as a case study to investigate the variations of echolocation calls when bats from one colony were introduced singly into the home cage of a new colony or two bats from different colonies were cohabitated together for one month. Our experiments showed that the single bat individual altered its peak frequency of echolocation calls to approach the call of new colony members and two bats from different colonies adjusted their call frequencies toward each other to a similar frequency after being chronically cohabitated. These results indicate that the 'compromise' in echolocation calls might be used to ensure effective mutual communication among bats.

‘Compromise’ in Echolocation Calls between Different Colonies of the Intermediate Leaf-Nosed Bat (Hipposideros larvatus)

PubMed Central

Chen, Yi; Liu, Qi; Su, Qianqian; Sun, Yunxiao; Peng, Xingwen; He, Xiangyang; Zhang, Libiao

2016-01-01

Each animal population has its own acoustic signature which facilitates identification, communication and reproduction. The sonar signals of bats can convey social information, such as species identity and contextual information. The goal of this study was to determine whether bats adjust their echolocation call structures to mutually recognize and communicate when they encounter the bats from different colonies. We used the intermediate leaf-nosed bats (Hipposideros larvatus) as a case study to investigate the variations of echolocation calls when bats from one colony were introduced singly into the home cage of a new colony or two bats from different colonies were cohabitated together for one month. Our experiments showed that the single bat individual altered its peak frequency of echolocation calls to approach the call of new colony members and two bats from different colonies adjusted their call frequencies toward each other to a similar frequency after being chronically cohabitated. These results indicate that the ‘compromise’ in echolocation calls might be used to ensure effective mutual communication among bats. PMID:27029005

Beyond Agar: Gel Substrates with Improved Optical Clarity and Drug Efficiency and Reduced Autofluorescence for Microbial Growth Experiments

PubMed Central

McElfresh, Cameron; Wong, Lily R.

2015-01-01

Agar, a seaweed extract, has been the standard support matrix for microbial experiments for over a century. Recent developments in high-throughput genetic screens have created a need to reevaluate the suitability of agar for use as colony support, as modern robotic printing systems now routinely spot thousands of colonies within the area of a single microtiter plate. Identifying optimal biophysical, biochemical, and biological properties of the gel support matrix in these extreme experimental conditions is instrumental to achieving the best possible reproducibility and sensitivity. Here we systematically evaluate a range of gelling agents by using the yeast Saccharomyces cerevisiae as a model microbe. We find that carrageenan and Phytagel have superior optical clarity and reduced autofluorescence, crucial for high-resolution imaging and fluorescent reporter screens. Nutrient choice and use of refined Noble agar or pure agarose reduce the effective dose of numerous selective drugs by >50%, potentially enabling large cost savings in genetic screens. Using thousands of mutant yeast strains to compare colony growth between substrates, we found no evidence of significant growth or nutrient biases between gel substrates, indicating that researchers could freely pick and choose the optimal gel for their respective application and experimental condition. PMID:26070672

Beyond Agar: Gel Substrates with Improved Optical Clarity and Drug Efficiency and Reduced Autofluorescence for Microbial Growth Experiments.

PubMed

Jaeger, Philipp A; McElfresh, Cameron; Wong, Lily R; Ideker, Trey

2015-08-15

Agar, a seaweed extract, has been the standard support matrix for microbial experiments for over a century. Recent developments in high-throughput genetic screens have created a need to reevaluate the suitability of agar for use as colony support, as modern robotic printing systems now routinely spot thousands of colonies within the area of a single microtiter plate. Identifying optimal biophysical, biochemical, and biological properties of the gel support matrix in these extreme experimental conditions is instrumental to achieving the best possible reproducibility and sensitivity. Here we systematically evaluate a range of gelling agents by using the yeast Saccharomyces cerevisiae as a model microbe. We find that carrageenan and Phytagel have superior optical clarity and reduced autofluorescence, crucial for high-resolution imaging and fluorescent reporter screens. Nutrient choice and use of refined Noble agar or pure agarose reduce the effective dose of numerous selective drugs by >50%, potentially enabling large cost savings in genetic screens. Using thousands of mutant yeast strains to compare colony growth between substrates, we found no evidence of significant growth or nutrient biases between gel substrates, indicating that researchers could freely pick and choose the optimal gel for their respective application and experimental condition. Copyright © 2015, American Society for Microbiology. All Rights Reserved.

Real-time optical monitoring of microbial growth using optimal combination of light-emitting diodes

NASA Astrophysics Data System (ADS)

Kobayashi, Ken-ichi; Yamada, Takeshi; Hiraishi, Akira; Nakauchi, Shigeki

2012-12-01

We developed a real-time optical monitoring system consisting of a monochrome complementary metal-oxide semiconductor (CMOS) camera and two light-emitting diodes (LEDs) with a constant temperature incubator for the rapid detection of microbial growth on solid media. As a target organism, we used Alicyclobacillus acidocaldarius, which is an acidophilic thermophilic endospore-forming bacterium able to survive in pasteurization processes and grow in acidic drink products such as apple juice. This bacterium was cultured on agar medium with a redox dye applied to improve detection sensitivity. On the basis of spectroscopic properties of the colony, medium, and LEDs, an optimal combination of two LED illuminations was selected to maximize the contrast between the colony and medium areas. We measured A. acidocaldarius and Escherichia coli at two different dilution levels using these two LEDs. From the results of time-course changes in the number of detected pixels in the detection images, a similar growth rate was estimated amongst the same species of microbes, regardless of the dilution level. This system has the ability to detect a colony of approximately 26 μm in diameter in a detection image, and it can be interpreted that the size corresponds to less than 20 μm diameter in visual inspection.

Genetic susceptibility, colony size, and water temperature drive white-pox disease on the coral Acropora palmata.

PubMed

Muller, Erinn M; van Woesik, Robert

2014-01-01

Outbreaks of coral diseases are one of the greatest threats to reef corals in the Caribbean, yet the mechanisms that lead to coral diseases are still largely unknown. Here we examined the spatial-temporal dynamics of white-pox disease on Acropora palmata coral colonies of known genotypes. We took a Bayesian approach, using Integrated Nested Laplace Approximation algorithms, to examine which covariates influenced the presence of white-pox disease over seven years. We showed that colony size, genetic susceptibility of the coral host, and high-water temperatures were the primary tested variables that were positively associated with the presence of white-pox disease on A. palmata colonies. Our study also showed that neither distance from previously diseased individuals, nor colony location, influenced the dynamics of white-pox disease. These results suggest that white-pox disease was most likely a consequence of anomalously high water temperatures that selectively compromised the oldest colonies and the most susceptible coral genotypes.

Genetic Susceptibility, Colony Size, and Water Temperature Drive White-Pox Disease on the Coral Acropora palmata

PubMed Central

Muller, Erinn M.; van Woesik, Robert

2014-01-01

Outbreaks of coral diseases are one of the greatest threats to reef corals in the Caribbean, yet the mechanisms that lead to coral diseases are still largely unknown. Here we examined the spatial-temporal dynamics of white-pox disease on Acropora palmata coral colonies of known genotypes. We took a Bayesian approach, using Integrated Nested Laplace Approximation algorithms, to examine which covariates influenced the presence of white-pox disease over seven years. We showed that colony size, genetic susceptibility of the coral host, and high-water temperatures were the primary tested variables that were positively associated with the presence of white-pox disease on A. palmata colonies. Our study also showed that neither distance from previously diseased individuals, nor colony location, influenced the dynamics of white-pox disease. These results suggest that white-pox disease was most likely a consequence of anomalously high water temperatures that selectively compromised the oldest colonies and the most susceptible coral genotypes. PMID:25372835

Rate Adaptive Based Resource Allocation with Proportional Fairness Constraints in OFDMA Systems

PubMed Central

Yin, Zhendong; Zhuang, Shufeng; Wu, Zhilu; Ma, Bo

2015-01-01

Orthogonal frequency division multiple access (OFDMA), which is widely used in the wireless sensor networks, allows different users to obtain different subcarriers according to their subchannel gains. Therefore, how to assign subcarriers and power to different users to achieve a high system sum rate is an important research area in OFDMA systems. In this paper, the focus of study is on the rate adaptive (RA) based resource allocation with proportional fairness constraints. Since the resource allocation is a NP-hard and non-convex optimization problem, a new efficient resource allocation algorithm ACO-SPA is proposed, which combines ant colony optimization (ACO) and suboptimal power allocation (SPA). To reduce the computational complexity, the optimization problem of resource allocation in OFDMA systems is separated into two steps. For the first one, the ant colony optimization algorithm is performed to solve the subcarrier allocation. Then, the suboptimal power allocation algorithm is developed with strict proportional fairness, and the algorithm is based on the principle that the sums of power and the reciprocal of channel-to-noise ratio for each user in different subchannels are equal. To support it, plenty of simulation results are presented. In contrast with root-finding and linear methods, the proposed method provides better performance in solving the proportional resource allocation problem in OFDMA systems. PMID:26426016

Using Ant Colony Optimization for Routing in VLSI Chips

NASA Astrophysics Data System (ADS)

Arora, Tamanna; Moses, Melanie

2009-04-01

Rapid advances in VLSI technology have increased the number of transistors that fit on a single chip to about two billion. A frequent problem in the design of such high performance and high density VLSI layouts is that of routing wires that connect such large numbers of components. Most wire-routing problems are computationally hard. The quality of any routing algorithm is judged by the extent to which it satisfies routing constraints and design objectives. Some of the broader design objectives include minimizing total routed wire length, and minimizing total capacitance induced in the chip, both of which serve to minimize power consumed by the chip. Ant Colony Optimization algorithms (ACO) provide a multi-agent framework for combinatorial optimization by combining memory, stochastic decision and strategies of collective and distributed learning by ant-like agents. This paper applies ACO to the NP-hard problem of finding optimal routes for interconnect routing on VLSI chips. The constraints on interconnect routing are used by ants as heuristics which guide their search process. We found that ACO algorithms were able to successfully incorporate multiple constraints and route interconnects on suite of benchmark chips. On an average, the algorithm routed with total wire length 5.5% less than other established routing algorithms.

Multi-scale modelling of the dynamics of cell colonies: insights into cell-adhesion forces and cancer invasion from in silico simulations.

PubMed

Schlüter, Daniela K; Ramis-Conde, Ignacio; Chaplain, Mark A J

2015-02-06

Studying the biophysical interactions between cells is crucial to understanding how normal tissue develops, how it is structured and also when malfunctions occur. Traditional experiments try to infer events at the tissue level after observing the behaviour of and interactions between individual cells. This approach assumes that cells behave in the same biophysical manner in isolated experiments as they do within colonies and tissues. In this paper, we develop a multi-scale multi-compartment mathematical model that accounts for the principal biophysical interactions and adhesion pathways not only at a cell-cell level but also at the level of cell colonies (in contrast to the traditional approach). Our results suggest that adhesion/separation forces between cells may be lower in cell colonies than traditional isolated single-cell experiments infer. As a consequence, isolated single-cell experiments may be insufficient to deduce important biological processes such as single-cell invasion after detachment from a solid tumour. The simulations further show that kinetic rates and cell biophysical characteristics such as pressure-related cell-cycle arrest have a major influence on cell colony patterns and can allow for the development of protrusive cellular structures as seen in invasive cancer cell lines independent of expression levels of pro-invasion molecules.

Multi-scale modelling of the dynamics of cell colonies: insights into cell-adhesion forces and cancer invasion from in silico simulations

PubMed Central

Schlüter, Daniela K.; Ramis-Conde, Ignacio; Chaplain, Mark A. J.

2015-01-01

Studying the biophysical interactions between cells is crucial to understanding how normal tissue develops, how it is structured and also when malfunctions occur. Traditional experiments try to infer events at the tissue level after observing the behaviour of and interactions between individual cells. This approach assumes that cells behave in the same biophysical manner in isolated experiments as they do within colonies and tissues. In this paper, we develop a multi-scale multi-compartment mathematical model that accounts for the principal biophysical interactions and adhesion pathways not only at a cell–cell level but also at the level of cell colonies (in contrast to the traditional approach). Our results suggest that adhesion/separation forces between cells may be lower in cell colonies than traditional isolated single-cell experiments infer. As a consequence, isolated single-cell experiments may be insufficient to deduce important biological processes such as single-cell invasion after detachment from a solid tumour. The simulations further show that kinetic rates and cell biophysical characteristics such as pressure-related cell-cycle arrest have a major influence on cell colony patterns and can allow for the development of protrusive cellular structures as seen in invasive cancer cell lines independent of expression levels of pro-invasion molecules. PMID:25519994

Environmental correlates of temporary emigration for female Weddell seals and consequences for recruitment

USGS Publications Warehouse

Stauffer, Glenn E.; Rotella, Jay J.; Garrott, Robert A.; Kendall, William L.

2014-01-01

In colonial-breeding species, prebreeders often emigrate temporarily from natal reproductive colonies then subsequently return for one or more years before producing young. Variation in attendance–nonattendance patterns can have implications for subsequent recruitment. We used open robust-design multistate models and 28 years of encounter data for prebreeding female Weddell seals (Leptonychotes weddellii [Lesson]) to evaluate hypotheses about (1) the relationships of temporary emigration (TE) probabilities to environmental and population size covariates and (2) motivations for attendance and consequences of nonattendance for subsequent probability of recruitment to the breeding population. TE probabilities were density dependent (βˆBPOP = 0.66,  = 0.17; estimated effects [β] and standard errors of population size in the previous year) and increased when the fast-ice edge was distant from the breeding colonies (βˆDIST = 0.75,  = 0.04; estimated effects and standard errors of distance to the sea-ice edge in the current year on TE probability in the current year) and were strongly age and state dependent. These results suggest that trade-offs between potential benefits and costs of colony attendance vary annually and might influence motivation to attend colonies. Recruitment probabilities were greatest for seals that consistently attended colonies in two or more years (e.g.,  = 0.56, SD = 0.17) and lowest for seals that never or inconsistently attended prior to recruitment (e.g.,  = 0.32, SD = 0.15), where denotes the mean recruitment probability (over all years) for 10-year-old seals for the specified prebreeder state. In colonial-breeding seabirds, repeated colony attendance increases subsequent probability of recruitment to the adult breeding population; our results suggest similar implications for a marine mammal and are consistent with the hypothesis that prebreeders were motivated to attend reproductive colonies to gain reproductive skills or perhaps to optimally synchronize estrus through close association with mature breeding females.

Improved artificial bee colony algorithm for vehicle routing problem with time windows

PubMed Central

Yan, Qianqian; Zhang, Mengjie; Yang, Yunong

2017-01-01

This paper investigates a well-known complex combinatorial problem known as the vehicle routing problem with time windows (VRPTW). Unlike the standard vehicle routing problem, each customer in the VRPTW is served within a given time constraint. This paper solves the VRPTW using an improved artificial bee colony (IABC) algorithm. The performance of this algorithm is improved by a local optimization based on a crossover operation and a scanning strategy. Finally, the effectiveness of the IABC is evaluated on some well-known benchmarks. The results demonstrate the power of IABC algorithm in solving the VRPTW. PMID:28961252

Ant colony optimization algorithm for interpretable Bayesian classifiers combination: application to medical predictions.

PubMed

Bouktif, Salah; Hanna, Eileen Marie; Zaki, Nazar; Abu Khousa, Eman

2014-01-01

Prediction and classification techniques have been well studied by machine learning researchers and developed for several real-word problems. However, the level of acceptance and success of prediction models are still below expectation due to some difficulties such as the low performance of prediction models when they are applied in different environments. Such a problem has been addressed by many researchers, mainly from the machine learning community. A second problem, principally raised by model users in different communities, such as managers, economists, engineers, biologists, and medical practitioners, etc., is the prediction models' interpretability. The latter is the ability of a model to explain its predictions and exhibit the causality relationships between the inputs and the outputs. In the case of classification, a successful way to alleviate the low performance is to use ensemble classiers. It is an intuitive strategy to activate collaboration between different classifiers towards a better performance than individual classier. Unfortunately, ensemble classifiers method do not take into account the interpretability of the final classification outcome. It even worsens the original interpretability of the individual classifiers. In this paper we propose a novel implementation of classifiers combination approach that does not only promote the overall performance but also preserves the interpretability of the resulting model. We propose a solution based on Ant Colony Optimization and tailored for the case of Bayesian classifiers. We validate our proposed solution with case studies from medical domain namely, heart disease and Cardiotography-based predictions, problems where interpretability is critical to make appropriate clinical decisions. The datasets, Prediction Models and software tool together with supplementary materials are available at http://faculty.uaeu.ac.ae/salahb/ACO4BC.htm.

Self-Organization in High-Density Bacterial Colonies: Efficient Crowd Control

PubMed Central

Campbell, Kyle; Melke, Pontus; Williams, Joshua W; Jedynak, Bruno; Stevens, Ann M; Groisman, Alex; Levchenko, Andre

2007-01-01

Colonies of bacterial cells can display complex collective dynamics, frequently culminating in the formation of biofilms and other ordered super-structures. Recent studies suggest that to cope with local environmental challenges, bacterial cells can actively seek out small chambers or cavities and assemble there, engaging in quorum sensing behavior. By using a novel microfluidic device, we showed that within chambers of distinct shapes and sizes allowing continuous cell escape, bacterial colonies can gradually self-organize. The directions of orientation of cells, their growth, and collective motion are mutually correlated and dictated by the chamber walls and locations of chamber exits. The ultimate highly organized steady state is conducive to a more-organized escape of cells from the chambers and increased access of nutrients into and evacuation of waste out of the colonies. Using a computational model, we suggest that the lengths of the cells might be optimized to maximize self-organization while minimizing the potential for stampede-like exit blockage. The self-organization described here may be crucial for the early stage of the organization of high-density bacterial colonies populating small, physically confined growth niches. It suggests that this phenomenon can play a critical role in bacterial biofilm initiation and development of other complex multicellular bacterial super-structures, including those implicated in infectious diseases. PMID:18044986

Offspring Size and Reproductive Allocation in Harvester Ants.

PubMed

Wiernasz, Diane C; Cole, Blaine J

2018-01-01

A fundamental decision that an organism must make is how to allocate resources to offspring, with respect to both size and number. The two major theoretical approaches to this problem, optimal offspring size and optimistic brood size models, make different predictions that may be reconciled by including how offspring fitness is related to size. We extended the reasoning of Trivers and Willard (1973) to derive a general model of how parents should allocate additional resources with respect to the number of males and females produced, and among individuals of each sex, based on the fitness payoffs of each. We then predicted how harvester ant colonies should invest additional resources and tested three hypotheses derived from our model, using data from 3 years of food supplementation bracketed by 6 years without food addition. All major results were predicted by our model: food supplementation increased the number of reproductives produced. Male, but not female, size increased with food addition; the greatest increases in male size occurred in colonies that made small females. We discuss how use of a fitness landscape improves quantitative predictions about allocation decisions. When parents can invest differentially in offspring of different types, the best strategy will depend on parental state as well as the effect of investment on offspring fitness.

An Enhanced Artificial Bee Colony Algorithm with Solution Acceptance Rule and Probabilistic Multisearch.

PubMed

Yurtkuran, Alkın; Emel, Erdal

2016-01-01

The artificial bee colony (ABC) algorithm is a popular swarm based technique, which is inspired from the intelligent foraging behavior of honeybee swarms. This paper proposes a new variant of ABC algorithm, namely, enhanced ABC with solution acceptance rule and probabilistic multisearch (ABC-SA) to address global optimization problems. A new solution acceptance rule is proposed where, instead of greedy selection between old solution and new candidate solution, worse candidate solutions have a probability to be accepted. Additionally, the acceptance probability of worse candidates is nonlinearly decreased throughout the search process adaptively. Moreover, in order to improve the performance of the ABC and balance the intensification and diversification, a probabilistic multisearch strategy is presented. Three different search equations with distinctive characters are employed using predetermined search probabilities. By implementing a new solution acceptance rule and a probabilistic multisearch approach, the intensification and diversification performance of the ABC algorithm is improved. The proposed algorithm has been tested on well-known benchmark functions of varying dimensions by comparing against novel ABC variants, as well as several recent state-of-the-art algorithms. Computational results show that the proposed ABC-SA outperforms other ABC variants and is superior to state-of-the-art algorithms proposed in the literature.

Art Education in Colonial India: Implementation and Imposition

ERIC Educational Resources Information Center

Kantawala, Ami

2012-01-01

Historical inquiry in art education forms the basis of any research undertaken in the field. It is on this path that we discover ignored moments and personalities and clarify challenging ideas, thus approaching history from multiple perspectives. This historical study attempts to reframe the past of colonial Indian art education within the broader…

The Cultural Production of the Not-Yet-Filipina/o-Subject in the Discourses of the Human Sciences

ERIC Educational Resources Information Center

Tavares, Hannah Maria

2006-01-01

In this article, the author relates two cases that foreshadow the cultural politics of colonial structures. Drawing upon poststructural, postcolonial, and psychoanalytic theoretical approaches, the author analyzes a range of textual productions manifested in dispersed sites that have formed the colonial image-repertoire and subjectification of…

Ant colony optimization algorithm for signal coordination of oversaturated traffic networks.

DOT National Transportation Integrated Search

2010-05-01

Traffic congestion is a daily and growing problem of the modern era in mostly all major cities in the world. : Increasing traffic demand strains the existing transportation system, leading to oversaturated network : conditions, especially at peak hou...

The alternative Pharaoh approach: stingless bees mummify beetle parasites alive

NASA Astrophysics Data System (ADS)

Greco, Mark K.; Hoffmann, Dorothee; Dollin, Anne; Duncan, Michael; Spooner-Hart, Robert; Neumann, Peter

2010-03-01

Workers from social insect colonies use different defence strategies to combat invaders. Nevertheless, some parasitic species are able to bypass colony defences. In particular, some beetle nest invaders cannot be killed or removed by workers of social bees, thus creating the need for alternative social defence strategies to ensure colony survival. Here we show, using diagnostic radioentomology, that stingless bee workers ( Trigona carbonaria) immediately mummify invading adult small hive beetles ( Aethina tumida) alive by coating them with a mixture of resin, wax and mud, thereby preventing severe damage to the colony. In sharp contrast to the responses of honeybee and bumblebee colonies, the rapid live mummification strategy of T. carbonaria effectively prevents beetle advancements and removes their ability to reproduce. The convergent evolution of mummification in stingless bees and encapsulation in honeybees is another striking example of co-evolution between insect societies and their parasites.

A traditional and a less-invasive robust design: choices in optimizing effort allocation for seabird population studies

USGS Publications Warehouse

Converse, S.J.; Kendall, W.L.; Doherty, P.F.; Naughton, M.B.; Hines, J.E.; Thomson, David L.; Cooch, Evan G.; Conroy, Michael J.

2009-01-01

For many animal populations, one or more life stages are not accessible to sampling, and therefore an unobservable state is created. For colonially-breeding populations, this unobservable state could represent the subset of adult breeders that have foregone breeding in a given year. This situation applies to many seabird populations, notably albatrosses, where skipped breeders are either absent from the colony, or are present but difficult to capture or correctly assign to breeding state. Kendall et al. have proposed design strategies for investigations of seabird demography where such temporary emigration occurs, suggesting the use of the robust design to permit the estimation of time-dependent parameters and to increase the precision of estimates from multi-state models. A traditional robust design, where animals are subject to capture multiple times in a sampling season, is feasible in many cases. However, due to concerns that multiple captures per season could cause undue disturbance to animals, Kendall et al. developed a less-invasive robust design (LIRD), where initial captures are followed by an assessment of the ratio of marked-to-unmarked birds in the population or sampled plot. This approach has recently been applied in the Northwestern Hawaiian Islands to populations of Laysan (Phoebastria immutabilis) and black-footed (P. nigripes) albatrosses. In this paper, we outline the LIRD and its application to seabird population studies. We then describe an approach to determining optimal allocation of sampling effort in which we consider a non-robust design option (nRD), and variations of both the traditional robust design (RD), and the LIRD. Variations we considered included the number of secondary sampling occasions for the RD and the amount of total effort allocated to the marked-to-unmarked ratio assessment for the LIRD. We used simulations, informed by early data from the Hawaiian study, to address optimal study design for our example cases. We found that the LIRD performed as well or nearly as well as certain variations of the RD in terms of root mean square error, especially when relatively little of the total effort was allocated to the assessment of the marked-to-unmarked ratio versus to initial captures. For the RD, we found no clear benefit of using 2, 4, or 6 secondary sampling occasions per year, though this result will depend on the relative effort costs of captures versus recaptures and on the length of the study. We also found that field-readable bands, which may be affixed to birds in addition to standard metal bands, will be beneficial in longer-term studies of albatrosses in the Northwestern Hawaiian Islands. Field-readable bands reduce the effort cost of recapturing individuals, and in the long-term this cost reduction can offset the additional effort expended in affixing the bands. Finally, our approach to determining optimal study design can be generally applied by researchers, with little seed data, to design their studies at the outset.

Efficient method to create integration-free, virus-free, Myc and Lin28-free human induced pluripotent stem cells from adherent cells.

PubMed

Kamath, Anant; Ternes, Sara; McGowan, Stephen; English, Anthony; Mallampalli, Rama; Moy, Alan B

2017-08-01

Nonviral induced pluripotent stem cell (IPSC) reprogramming is not efficient without the oncogenes, Myc and Lin28 . We describe a robust Myc and Lin28 -free IPSC reprogramming approach using reprogramming molecules. IPSC colony formation was compared in the presence and absence of Myc and Lin28 by the mixture of reprogramming molecules and episomal vectors. While more colonies were observed in cultures transfected with the aforementioned oncogenes, the Myc and Lin28 -free method achieved the same reprogramming efficiency as reports that used these oncogenes. Further, all colonies were fully reprogrammed based on expression of SSEA4, even in the absence of Myc and Lin28 . This approach satisfies an important regulatory pathway for developing IPSC cell therapies with lower clinical risk.

Improved biovolume estimation of Microcystis aeruginosa colonies: A statistical approach.

PubMed

Alcántara, I; Piccini, C; Segura, A M; Deus, S; González, C; Martínez de la Escalera, G; Kruk, C

2018-05-27

The Microcystis aeruginosa complex (MAC) clusters many of the most common freshwater and brackish bloom-forming cyanobacteria. In monitoring protocols, biovolume estimation is a common approach to determine MAC colonies biomass and useful for prediction purposes. Biovolume (μm 3 mL -1 ) is calculated multiplying organism abundance (orgL -1 ) by colonial volume (μm 3 org -1 ). Colonial volume is estimated based on geometric shapes and requires accurate measurements of dimensions using optical microscopy. A trade-off between easy-to-measure but low-accuracy simple shapes (e.g. sphere) and time costly but high-accuracy complex shapes (e.g. ellipsoid) volume estimation is posed. Overestimations effects in ecological studies and management decisions associated to harmful blooms are significant due to the large sizes of MAC colonies. In this work, we aimed to increase the precision of MAC biovolume estimations by developing a statistical model based on two easy-to-measure dimensions. We analyzed field data from a wide environmental gradient (800 km) spanning freshwater to estuarine and seawater. We measured length, width and depth from ca. 5700 colonies under an inverted microscope and estimated colonial volume using three different recommended geometrical shapes (sphere, prolate spheroid and ellipsoid). Because of the non-spherical shape of MAC the ellipsoid resulted in the most accurate approximation, whereas the sphere overestimated colonial volume (3-80) especially for large colonies (MLD higher than 300 μm). Ellipsoid requires measuring three dimensions and is time-consuming. Therefore, we constructed different statistical models to predict organisms depth based on length and width. Splitting the data into training (2/3) and test (1/3) sets, all models resulted in low training (1.41-1.44%) and testing average error (1.3-2.0%). The models were also evaluated using three other independent datasets. The multiple linear model was finally selected to calculate MAC volume as an ellipsoid based on length and width. This work contributes to achieve a better estimation of MAC volume applicable to monitoring programs as well as to ecological research. Copyright © 2017. Published by Elsevier B.V.

Ant colony optimisation-direct cover: a hybrid ant colony direct cover technique for multi-level synthesis of multiple-valued logic functions

NASA Astrophysics Data System (ADS)

Abd-El-Barr, Mostafa

2010-12-01

The use of non-binary (multiple-valued) logic in the synthesis of digital systems can lead to savings in chip area. Advances in very large scale integration (VLSI) technology have enabled the successful implementation of multiple-valued logic (MVL) circuits. A number of heuristic algorithms for the synthesis of (near) minimal sum-of products (two-level) realisation of MVL functions have been reported in the literature. The direct cover (DC) technique is one such algorithm. The ant colony optimisation (ACO) algorithm is a meta-heuristic that uses constructive greediness to explore a large solution space in finding (near) optimal solutions. The ACO algorithm mimics the ant's behaviour in the real world in using the shortest path to reach food sources. We have previously introduced an ACO-based heuristic for the synthesis of two-level MVL functions. In this article, we introduce the ACO-DC hybrid technique for the synthesis of multi-level MVL functions. The basic idea is to use an ant to decompose a given MVL function into a number of levels and then synthesise each sub-function using a DC-based technique. The results obtained using the proposed approach are compared to those obtained using existing techniques reported in the literature. A benchmark set consisting of 50,000 randomly generated 2-variable 4-valued functions is used in the comparison. The results obtained using the proposed ACO-DC technique are shown to produce efficient realisation in terms of the average number of gates (as a measure of chip area) needed for the synthesis of a given MVL function.

Evaluating exposure and potential effects on honeybee brood (Apis mellifera) development using glyphosate as an example

PubMed Central

Thompson, Helen M; Levine, Steven L; Doering, Janine; Norman, Steve; Manson, Philip; Sutton, Peter; von Mérey, Georg

2014-01-01

This study aimed to develop an approach to evaluate potential effects of plant protection products on honeybee brood with colonies at realistic worst-case exposure rates. The approach comprised 2 stages. In the first stage, honeybee colonies were exposed to a commercial formulation of glyphosate applied to flowering Phacelia tanacetifolia with glyphosate residues quantified in relevant matrices (pollen and nectar) collected by foraging bees on days 1, 2, 3, 4, and 7 postapplication and glyphosate levels in larvae were measured on days 4 and 7. Glyphosate levels in pollen were approximately 10 times higher than in nectar and glyphosate demonstrated rapid decline in both matrices. Residue data along with foraging rates and food requirements of the colony were then used to set dose rates in the effects study. In the second stage, the toxicity of technical glyphosate to developing honeybee larvae and pupae, and residues in larvae, were then determined by feeding treated sucrose directly to honeybee colonies at dose rates that reflect worst-case exposure scenarios. There were no significant effects from glyphosate observed in brood survival, development, and mean pupal weight. Additionally, there were no biologically significant levels of adult mortality observed in any glyphosate treatment group. Significant effects were observed only in the fenoxycarb toxic reference group and included increased brood mortality and a decline in the numbers of bees and brood. Mean glyphosate residues in larvae were comparable at 4 days after spray application in the exposure study and also following dosing at a level calculated from the mean measured levels in pollen and nectar, showing the applicability and robustness of the approach for dose setting with honeybee brood studies. This study has developed a versatile and predictive approach for use in higher tier honeybee toxicity studies. It can be used to realistically quantify exposure of colonies to pesticides to allow the appropriate dose rates to be determined, based on realistic worst-case residues in pollen and nectar and estimated intake by the colony, as shown by the residue analysis. Previous studies have used the standard methodology developed primarily to identify pesticides with insect-growth disrupting properties of pesticide formulations, which are less reliant on identifying realistic exposure scenarios. However, this adaptation of the method can be used to determine dose–response effects of colony level exposure to pesticides with a wide range of properties. This approach would limit the number of replicated tunnel or field-scale studies that need to be undertaken to assess effects on honeybee brood and may be of particular benefit where residues in pollen and nectar are crop- and/or formulation-specific, such as systemic seed treatments and granular applications. Integr Environ Assess Manag 2014;10:463–470. PMID:24616275

Evaluating exposure and potential effects on honeybee brood (Apis mellifera) development using glyphosate as an example.

PubMed

Thompson, Helen M; Levine, Steven L; Doering, Janine; Norman, Steve; Manson, Philip; Sutton, Peter; von Mérey, Georg

2014-07-01

This study aimed to develop an approach to evaluate potential effects of plant protection products on honeybee brood with colonies at realistic worst-case exposure rates. The approach comprised 2 stages. In the first stage, honeybee colonies were exposed to a commercial formulation of glyphosate applied to flowering Phacelia tanacetifolia with glyphosate residues quantified in relevant matrices (pollen and nectar) collected by foraging bees on days 1, 2, 3, 4, and 7 postapplication and glyphosate levels in larvae were measured on days 4 and 7. Glyphosate levels in pollen were approximately 10 times higher than in nectar and glyphosate demonstrated rapid decline in both matrices. Residue data along with foraging rates and food requirements of the colony were then used to set dose rates in the effects study. In the second stage, the toxicity of technical glyphosate to developing honeybee larvae and pupae, and residues in larvae, were then determined by feeding treated sucrose directly to honeybee colonies at dose rates that reflect worst-case exposure scenarios. There were no significant effects from glyphosate observed in brood survival, development, and mean pupal weight. Additionally, there were no biologically significant levels of adult mortality observed in any glyphosate treatment group. Significant effects were observed only in the fenoxycarb toxic reference group and included increased brood mortality and a decline in the numbers of bees and brood. Mean glyphosate residues in larvae were comparable at 4 days after spray application in the exposure study and also following dosing at a level calculated from the mean measured levels in pollen and nectar, showing the applicability and robustness of the approach for dose setting with honeybee brood studies. This study has developed a versatile and predictive approach for use in higher tier honeybee toxicity studies. It can be used to realistically quantify exposure of colonies to pesticides to allow the appropriate dose rates to be determined, based on realistic worst-case residues in pollen and nectar and estimated intake by the colony, as shown by the residue analysis. Previous studies have used the standard methodology developed primarily to identify pesticides with insect-growth disrupting properties of pesticide formulations, which are less reliant on identifying realistic exposure scenarios. However, this adaptation of the method can be used to determine dose-response effects of colony level exposure to pesticides with a wide range of properties. This approach would limit the number of replicated tunnel or field-scale studies that need to be undertaken to assess effects on honeybee brood and may be of particular benefit where residues in pollen and nectar are crop- and/or formulation-specific, such as systemic seed treatments and granular applications. © 2014 The Authors. Integrated Environmental Assessment and Management Published by SETAC.

Applying a multiobjective metaheuristic inspired by honey bees to phylogenetic inference.

PubMed

Santander-Jiménez, Sergio; Vega-Rodríguez, Miguel A

2013-10-01

The development of increasingly popular multiobjective metaheuristics has allowed bioinformaticians to deal with optimization problems in computational biology where multiple objective functions must be taken into account. One of the most relevant research topics that can benefit from these techniques is phylogenetic inference. Throughout the years, different researchers have proposed their own view about the reconstruction of ancestral evolutionary relationships among species. As a result, biologists often report different phylogenetic trees from a same dataset when considering distinct optimality principles. In this work, we detail a multiobjective swarm intelligence approach based on the novel Artificial Bee Colony algorithm for inferring phylogenies. The aim of this paper is to propose a complementary view of phylogenetics according to the maximum parsimony and maximum likelihood criteria, in order to generate a set of phylogenetic trees that represent a compromise between these principles. Experimental results on a variety of nucleotide data sets and statistical studies highlight the relevance of the proposal with regard to other multiobjective algorithms and state-of-the-art biological methods. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Improving Societal Resilience Through Enhanced Reconnection Speed of Damaged Networks

NASA Astrophysics Data System (ADS)

Vodák, Rostislav; Bíl, Michal

2017-04-01

Road networks rank among the foundations of civilization. They enable people, services and goods to be transported to arbitrary places at any time. Its functioning can be impacted by various events, not only by natural hazards and their combinations. This can lead to the concurrent interruption of a number of roads and even cut-off parts of the network from vital services. The impact of these events can be reduced by various measures, but cannot be fully eliminated. We are aware of the fact that extreme events which result in road network break up will occur regardless of the ongoing process of hazard reduction using, for example, the improvement of the structural robustness of roads. The next problem is that many of the events are unpredictable and thus the needed costs of the improvement can easily spiral out of control. We therefore focus on the speed of the recovery process which can be optimized. This means that the time during which the damaged network is reconnected again will be as short as possible. The result of the optimization procedure is a sequence of road links which represent the routes of the repair units. The optimization process is, however, highly nontrivial because of the large number of possible routes for repair units. This prevents anyone from finding an optimal solution. We consequently introduce an approach based on the Ant Colony Optimization algorithm which is able to suggest an almost optimal solution under various constraints which can be established by the administrator of the network. We will also demonstrate its results and variability with several case examples.

A Multi-Attribute Pheromone Ant Secure Routing Algorithm Based on Reputation Value for Sensor Networks

PubMed Central

Zhang, Lin; Yin, Na; Fu, Xiong; Lin, Qiaomin; Wang, Ruchuan

2017-01-01

With the development of wireless sensor networks, certain network problems have become more prominent, such as limited node resources, low data transmission security, and short network life cycles. To solve these problems effectively, it is important to design an efficient and trusted secure routing algorithm for wireless sensor networks. Traditional ant-colony optimization algorithms exhibit only local convergence, without considering the residual energy of the nodes and many other problems. This paper introduces a multi-attribute pheromone ant secure routing algorithm based on reputation value (MPASR). This algorithm can reduce the energy consumption of a network and improve the reliability of the nodes’ reputations by filtering nodes with higher coincidence rates and improving the method used to update the nodes’ communication behaviors. At the same time, the node reputation value, the residual node energy and the transmission delay are combined to formulate a synthetic pheromone that is used in the formula for calculating the random proportion rule in traditional ant-colony optimization to select the optimal data transmission path. Simulation results show that the improved algorithm can increase both the security of data transmission and the quality of routing service. PMID:28282894

An Exploration of Linguistic Neo-Colonialism through Educational Language Policy--An Irish Perspective

ERIC Educational Resources Information Center

Dillon, Anna

2016-01-01

In this paper, educational language policy is explored through the lens of linguistic neo-colonialism in Ireland in the case of learners of English as an Additional Language. The perspective of Ireland as a decolonized nation may have an impact on current language policy. Arguments for an additive approach to language and identity, language…

A Stochastic Inversion Method for Potential Field Data: Ant Colony Optimization

NASA Astrophysics Data System (ADS)

Liu, Shuang; Hu, Xiangyun; Liu, Tianyou

2014-07-01

Simulating natural ants' foraging behavior, the ant colony optimization (ACO) algorithm performs excellently in combinational optimization problems, for example the traveling salesman problem and the quadratic assignment problem. However, the ACO is seldom used to inverted for gravitational and magnetic data. On the basis of the continuous and multi-dimensional objective function for potential field data optimization inversion, we present the node partition strategy ACO (NP-ACO) algorithm for inversion of model variables of fixed shape and recovery of physical property distributions of complicated shape models. We divide the continuous variables into discrete nodes and ants directionally tour the nodes by use of transition probabilities. We update the pheromone trails by use of Gaussian mapping between the objective function value and the quantity of pheromone. It can analyze the search results in real time and promote the rate of convergence and precision of inversion. Traditional mapping, including the ant-cycle system, weaken the differences between ant individuals and lead to premature convergence. We tested our method by use of synthetic data and real data from scenarios involving gravity and magnetic anomalies. The inverted model variables and recovered physical property distributions were in good agreement with the true values. The ACO algorithm for binary representation imaging and full imaging can recover sharper physical property distributions than traditional linear inversion methods. The ACO has good optimization capability and some excellent characteristics, for example robustness, parallel implementation, and portability, compared with other stochastic metaheuristics.

Optimization on Paddy Crops in Central Java (with Solver, SVD on Least Square and ACO (Ant Colony Algorithm))

NASA Astrophysics Data System (ADS)

Parhusip, H. A.; Trihandaru, S.; Susanto, B.; Prasetyo, S. Y. J.; Agus, Y. H.; Simanjuntak, B. H.

2017-03-01

Several algorithms and objective functions on paddy crops have been studied to get optimal paddy crops in Central Java based on the data given from Surakarta and Boyolali. The algorithms are linear solver, least square and Ant Colony Algorithms (ACO) to develop optimization procedures on paddy crops modelled with Modified GSTAR (Generalized Space-Time Autoregressive) and nonlinear models where the nonlinear models are quadratic and power functions. The studied data contain paddy crops from Surakarta and Boyolali determining the best period of planting in the year 1992-2012 for Surakarta where 3 periods for planting are known and the optimal amount of paddy crops in Boyolali in the year 2008-2013. Having these analyses may guide the local agriculture government to give a decision on rice sustainability in its region. The best period for planting in Surakarta is observed, i.e. the best period is in September-December based on the data 1992-2012 by considering the planting area, the cropping area, and the paddy crops are the most important factors to be taken into account. As a result, we can refer the paddy crops in this best period (about 60.4 thousand tons per year) as the optimal results in 1992-2012 where the used objective function is quadratic. According to the research, the optimal paddy crops in Boyolali about 280 thousand tons per year where the studied factors are the amount of rainfalls, the harvested area and the paddy crops in 2008-2013. In this case, linear and power functions are studied to be the objective functions. Compared to all studied algorithms, the linear solver is still recommended to be an optimization tool for a local agriculture government to predict paddy crops in future.

An enhanced artificial bee colony algorithm (EABC) for solving dispatching of hydro-thermal system (DHTS) problem

PubMed Central

Yu, Yi; Hu, Binqi; Liu, Xinglong

2018-01-01

The dispatching of hydro-thermal system is a nonlinear programming problem with multiple constraints and high dimensions and the solution techniques of the model have been a hotspot in research. Based on the advantage of that the artificial bee colony algorithm (ABC) can efficiently solve the high-dimensional problem, an improved artificial bee colony algorithm has been proposed to solve DHTS problem in this paper. The improvements of the proposed algorithm include two aspects. On one hand, local search can be guided in efficiency by the information of the global optimal solution and its gradient in each generation. The global optimal solution improves the search efficiency of the algorithm but loses diversity, while the gradient can weaken the loss of diversity caused by the global optimal solution. On the other hand, inspired by genetic algorithm, the nectar resource which has not been updated in limit generation is transformed to a new one by using selection, crossover and mutation, which can ensure individual diversity and make full use of prior information for improving the global search ability of the algorithm. The two improvements of ABC algorithm are proved to be effective via a classical numeral example at last. Among which the genetic operator for the promotion of the ABC algorithm’s performance is significant. The results are also compared with those of other state-of-the-art algorithms, the enhanced ABC algorithm has general advantages in minimum cost, average cost and maximum cost which shows its usability and effectiveness. The achievements in this paper provide a new method for solving the DHTS problems, and also offer a novel reference for the improvement of mechanism and the application of algorithms. PMID:29324743

Urban Growth Modeling Using Cellular Automata with Multi-Temporal Remote Sensing Images Calibrated by the Artificial Bee Colony Optimization Algorithm.

PubMed

Naghibi, Fereydoun; Delavar, Mahmoud Reza; Pijanowski, Bryan

2016-12-14

Cellular Automata (CA) is one of the most common techniques used to simulate the urbanization process. CA-based urban models use transition rules to deliver spatial patterns of urban growth and urban dynamics over time. Determining the optimum transition rules of the CA is a critical step because of the heterogeneity and nonlinearities existing among urban growth driving forces. Recently, new CA models integrated with optimization methods based on swarm intelligence algorithms were proposed to overcome this drawback. The Artificial Bee Colony (ABC) algorithm is an advanced meta-heuristic swarm intelligence-based algorithm. Here, we propose a novel CA-based urban change model that uses the ABC algorithm to extract optimum transition rules. We applied the proposed ABC-CA model to simulate future urban growth in Urmia (Iran) with multi-temporal Landsat images from 1997, 2006 and 2015. Validation of the simulation results was made through statistical methods such as overall accuracy, the figure of merit and total operating characteristics (TOC). Additionally, we calibrated the CA model by ant colony optimization (ACO) to assess the performance of our proposed model versus similar swarm intelligence algorithm methods. We showed that the overall accuracy and the figure of merit of the ABC-CA model are 90.1% and 51.7%, which are 2.9% and 8.8% higher than those of the ACO-CA model, respectively. Moreover, the allocation disagreement of the simulation results for the ABC-CA model is 9.9%, which is 2.9% less than that of the ACO-CA model. Finally, the ABC-CA model also outperforms the ACO-CA model with fewer quantity and allocation errors and slightly more hits.

Urban Growth Modeling Using Cellular Automata with Multi-Temporal Remote Sensing Images Calibrated by the Artificial Bee Colony Optimization Algorithm

PubMed Central

Naghibi, Fereydoun; Delavar, Mahmoud Reza; Pijanowski, Bryan

2016-01-01

Cellular Automata (CA) is one of the most common techniques used to simulate the urbanization process. CA-based urban models use transition rules to deliver spatial patterns of urban growth and urban dynamics over time. Determining the optimum transition rules of the CA is a critical step because of the heterogeneity and nonlinearities existing among urban growth driving forces. Recently, new CA models integrated with optimization methods based on swarm intelligence algorithms were proposed to overcome this drawback. The Artificial Bee Colony (ABC) algorithm is an advanced meta-heuristic swarm intelligence-based algorithm. Here, we propose a novel CA-based urban change model that uses the ABC algorithm to extract optimum transition rules. We applied the proposed ABC-CA model to simulate future urban growth in Urmia (Iran) with multi-temporal Landsat images from 1997, 2006 and 2015. Validation of the simulation results was made through statistical methods such as overall accuracy, the figure of merit and total operating characteristics (TOC). Additionally, we calibrated the CA model by ant colony optimization (ACO) to assess the performance of our proposed model versus similar swarm intelligence algorithm methods. We showed that the overall accuracy and the figure of merit of the ABC-CA model are 90.1% and 51.7%, which are 2.9% and 8.8% higher than those of the ACO-CA model, respectively. Moreover, the allocation disagreement of the simulation results for the ABC-CA model is 9.9%, which is 2.9% less than that of the ACO-CA model. Finally, the ABC-CA model also outperforms the ACO-CA model with fewer quantity and allocation errors and slightly more hits. PMID:27983633

An enhanced artificial bee colony algorithm (EABC) for solving dispatching of hydro-thermal system (DHTS) problem.

PubMed

Yu, Yi; Wu, Yonggang; Hu, Binqi; Liu, Xinglong

2018-01-01

The dispatching of hydro-thermal system is a nonlinear programming problem with multiple constraints and high dimensions and the solution techniques of the model have been a hotspot in research. Based on the advantage of that the artificial bee colony algorithm (ABC) can efficiently solve the high-dimensional problem, an improved artificial bee colony algorithm has been proposed to solve DHTS problem in this paper. The improvements of the proposed algorithm include two aspects. On one hand, local search can be guided in efficiency by the information of the global optimal solution and its gradient in each generation. The global optimal solution improves the search efficiency of the algorithm but loses diversity, while the gradient can weaken the loss of diversity caused by the global optimal solution. On the other hand, inspired by genetic algorithm, the nectar resource which has not been updated in limit generation is transformed to a new one by using selection, crossover and mutation, which can ensure individual diversity and make full use of prior information for improving the global search ability of the algorithm. The two improvements of ABC algorithm are proved to be effective via a classical numeral example at last. Among which the genetic operator for the promotion of the ABC algorithm's performance is significant. The results are also compared with those of other state-of-the-art algorithms, the enhanced ABC algorithm has general advantages in minimum cost, average cost and maximum cost which shows its usability and effectiveness. The achievements in this paper provide a new method for solving the DHTS problems, and also offer a novel reference for the improvement of mechanism and the application of algorithms.

End to End Digitisation and Analysis of Three-Dimensional Coral Models, from Communities to Corallites.

PubMed

Gutierrez-Heredia, Luis; Benzoni, Francesca; Murphy, Emma; Reynaud, Emmanuel G

2016-01-01

Coral reefs hosts nearly 25% of all marine species and provide food sources for half a billion people worldwide while only a very small percentage have been surveyed. Advances in technology and processing along with affordable underwater cameras and Internet availability gives us the possibility to provide tools and softwares to survey entire coral reefs. Holistic ecological analyses of corals require not only the community view (10s to 100s of meters), but also the single colony analysis as well as corallite identification. As corals are three-dimensional, classical approaches to determine percent cover and structural complexity across spatial scales are inefficient, time-consuming and limited to experts. Here we propose an end-to-end approach to estimate these parameters using low-cost equipment (GoPro, Canon) and freeware (123D Catch, Meshmixer and Netfabb), allowing every community to participate in surveys and monitoring of their coral ecosystem. We demonstrate our approach on 9 species of underwater colonies in ranging size and morphology. 3D models of underwater colonies, fresh samples and bleached skeletons with high quality texture mapping and detailed topographic morphology were produced, and Surface Area and Volume measurements (parameters widely used for ecological and coral health studies) were calculated and analysed. Moreover, we integrated collected sample models with micro-photogrammetry models of individual corallites to aid identification and colony and polyp scale analysis.

End to End Digitisation and Analysis of Three-Dimensional Coral Models, from Communities to Corallites

PubMed Central

Gutierrez-Heredia, Luis; Benzoni, Francesca; Murphy, Emma; Reynaud, Emmanuel G.

2016-01-01

Coral reefs hosts nearly 25% of all marine species and provide food sources for half a billion people worldwide while only a very small percentage have been surveyed. Advances in technology and processing along with affordable underwater cameras and Internet availability gives us the possibility to provide tools and softwares to survey entire coral reefs. Holistic ecological analyses of corals require not only the community view (10s to 100s of meters), but also the single colony analysis as well as corallite identification. As corals are three-dimensional, classical approaches to determine percent cover and structural complexity across spatial scales are inefficient, time-consuming and limited to experts. Here we propose an end-to-end approach to estimate these parameters using low-cost equipment (GoPro, Canon) and freeware (123D Catch, Meshmixer and Netfabb), allowing every community to participate in surveys and monitoring of their coral ecosystem. We demonstrate our approach on 9 species of underwater colonies in ranging size and morphology. 3D models of underwater colonies, fresh samples and bleached skeletons with high quality texture mapping and detailed topographic morphology were produced, and Surface Area and Volume measurements (parameters widely used for ecological and coral health studies) were calculated and analysed. Moreover, we integrated collected sample models with micro-photogrammetry models of individual corallites to aid identification and colony and polyp scale analysis. PMID:26901845

Roosting and foraging social structure of the endangered Indiana bat (Myotis sodalis)

USGS Publications Warehouse

Silvis, Alexander; Kniowski, Andrew B.; Gehrt, Stanley D.; Ford, W. Mark

2014-01-01

Social dynamics are an important but poorly understood aspect of bat ecology. Herein we use a combination of graph theoretic and spatial approaches to describe the roost and social network characteristics and foraging associations of an Indiana bat (Myotis sodalis) maternity colony in an agricultural landscape in Ohio, USA. We tracked 46 bats to 50 roosts (423 total relocations) and collected 2,306 foraging locations for 40 bats during the summers of 2009 and 2010. We found the colony roosting network was highly centralized in both years and that roost and social networks differed significantly from random networks. Roost and social network structure also differed substantially between years. Social network structure appeared to be unrelated to segregation of roosts between age classes. For bats whose individual foraging ranges were calculated, many shared foraging space with at least one other bat. Compared across all possible bat dyads, 47% and 43% of the dyads showed more than expected overlap of foraging areas in 2009 and 2010 respectively. Colony roosting area differed between years, but the roosting area centroid shifted only 332 m. In contrast, whole colony foraging area use was similar between years. Random roost removal simulations suggest that Indiana bat colonies may be robust to loss of a limited number of roosts but may respond differently from year to year. Our study emphasizes the utility of graphic theoretic and spatial approaches for examining the sociality and roosting behavior of bats. Detailed knowledge of the relationships between social and spatial aspects of bat ecology could greatly increase conservation effectiveness by allowing more structured approaches to roost and habitat retention for tree-roosting, socially-aggregating bat species.

Roosting and foraging social structure of the endangered Indiana bat (Myotis sodalis).

PubMed

Silvis, Alexander; Kniowski, Andrew B; Gehrt, Stanley D; Ford, W Mark

2014-01-01

Social dynamics are an important but poorly understood aspect of bat ecology. Herein we use a combination of graph theoretic and spatial approaches to describe the roost and social network characteristics and foraging associations of an Indiana bat (Myotis sodalis) maternity colony in an agricultural landscape in Ohio, USA. We tracked 46 bats to 50 roosts (423 total relocations) and collected 2,306 foraging locations for 40 bats during the summers of 2009 and 2010. We found the colony roosting network was highly centralized in both years and that roost and social networks differed significantly from random networks. Roost and social network structure also differed substantially between years. Social network structure appeared to be unrelated to segregation of roosts between age classes. For bats whose individual foraging ranges were calculated, many shared foraging space with at least one other bat. Compared across all possible bat dyads, 47% and 43% of the dyads showed more than expected overlap of foraging areas in 2009 and 2010 respectively. Colony roosting area differed between years, but the roosting area centroid shifted only 332 m. In contrast, whole colony foraging area use was similar between years. Random roost removal simulations suggest that Indiana bat colonies may be robust to loss of a limited number of roosts but may respond differently from year to year. Our study emphasizes the utility of graphic theoretic and spatial approaches for examining the sociality and roosting behavior of bats. Detailed knowledge of the relationships between social and spatial aspects of bat ecology could greatly increase conservation effectiveness by allowing more structured approaches to roost and habitat retention for tree-roosting, socially-aggregating bat species.

Morphology and dynamics of tumor cell colonies propagating in epidermal growth factor supplemented media

NASA Astrophysics Data System (ADS)

Muzzio, N. E.; Carballido, M.; Pasquale, M. A.; González, P. H.; Azzaroni, O.; Arvia, A. J.

2018-07-01

The epidermal growth factor (EGF) plays a key role in physiological and pathological processes. This work reports on the influence of EGF concentration (c EGF) on the modulation of individual cell phenotype and cell colony kinetics with the aim of perturbing the colony front roughness fluctuations. For this purpose, HeLa cell colonies that remain confluent along the whole expansion process with initial quasi-radial geometry and different initial cell populations, as well as colonies with initial quasi-linear geometry and large cell population, are employed. Cell size and morphology as well as its adhesive characteristics depend on c EGF. Quasi-radial colonies (QRC) expansion kinetics in EGF-containing medium exhibits a complex behavior. Namely, at the first stages of growth, the average QRC radius evolution can be described by a t 1/2 diffusion term coupled with exponential growth kinetics up to a critical time, and afterwards a growth regime approaching constant velocity. The extension of each regime depends on c EGF and colony history. In the presence of EGF, the initial expansion of quasi-linear colonies (QLCs) also exhibits morphological changes at both the cell and the colony levels. In these cases, the cell density at the colony border region becomes smaller than in the absence of EGF and consequently, the extension of the effective rim where cell duplication and motility contribute to the colony expansion increases. QLC front displacement velocity increases with c EGF up to a maximum value in the 2–10 ng ml‑1 range. Individual cell velocity is increased by EGF, and an enhancement in both the persistence and the ballistic characteristics of cell trajectories can be distinguished. For an intermediate c EGF, collective cell displacements contribute to the roughening of the colony contours. This global dynamics becomes compatible with the standard Kardar–Parisi–Zhang growth model, although a faster colony roughness saturation in EGF-containing medium than in the control medium is observed.

Application of hybrid artificial fish swarm algorithm based on similar fragments in VRP

NASA Astrophysics Data System (ADS)

Che, Jinnuo; Zhou, Kang; Zhang, Xueyu; Tong, Xin; Hou, Lingyun; Jia, Shiyu; Zhen, Yiting

2018-03-01

Focused on the issue that the decrease of convergence speed and the precision of calculation at the end of the process in Artificial Fish Swarm Algorithm(AFSA) and instability of results, a hybrid AFSA based on similar fragments is proposed. Traditional AFSA enjoys a lot of obvious advantages in solving complex optimization problems like Vehicle Routing Problem(VRP). AFSA have a few limitations such as low convergence speed, low precision and instability of results. In this paper, two improvements are introduced. On the one hand, change the definition of the distance for artificial fish, as well as increase vision field of artificial fish, and the problem of speed and precision can be improved when solving VRP. On the other hand, mix artificial bee colony algorithm(ABC) into AFSA - initialize the population of artificial fish by the ABC, and it solves the problem of instability of results in some extend. The experiment results demonstrate that the optimal solution of the hybrid AFSA is easier to approach the optimal solution of the standard database than the other two algorithms. In conclusion, the hybrid algorithm can effectively solve the problem that instability of results and decrease of convergence speed and the precision of calculation at the end of the process.

A spatially informative optic flow model of bee colony with saccadic flight strategy for global optimization.

PubMed

Das, Swagatam; Biswas, Subhodip; Panigrahi, Bijaya K; Kundu, Souvik; Basu, Debabrota

2014-10-01

This paper presents a novel search metaheuristic inspired from the physical interpretation of the optic flow of information in honeybees about the spatial surroundings that help them orient themselves and navigate through search space while foraging. The interpreted behavior combined with the minimal foraging is simulated by the artificial bee colony algorithm to develop a robust search technique that exhibits elevated performance in multidimensional objective space. Through detailed experimental study and rigorous analysis, we highlight the statistical superiority enjoyed by our algorithm over a wide variety of functions as compared to some highly competitive state-of-the-art methods.

An artificial bee colony algorithm for locating the critical slip surface in slope stability analysis

NASA Astrophysics Data System (ADS)

Kang, Fei; Li, Junjie; Ma, Zhenyue

2013-02-01

Determination of the critical slip surface with the minimum factor of safety of a slope is a difficult constrained global optimization problem. In this article, an artificial bee colony algorithm with a multi-slice adjustment method is proposed for locating the critical slip surfaces of soil slopes, and the Spencer method is employed to calculate the factor of safety. Six benchmark examples are presented to illustrate the reliability and efficiency of the proposed technique, and it is also compared with some well-known or recent algorithms for the problem. The results show that the new algorithm is promising in terms of accuracy and efficiency.

Varroa destructor: research avenues towards sustainable control

USDA-ARS?s Scientific Manuscript database

Pollination by honeybees plays a key role in the functioning of ecosystems and optimization of agricultural yields. Severe honeybee colony losses worldwide have raised concerns about the sustainability of these pollination services. In most cases, bee morbidity appears to be the product of many inte...

CMOS analogue amplifier circuits optimisation using hybrid backtracking search algorithm with differential evolution

NASA Astrophysics Data System (ADS)

Mallick, S.; Kar, R.; Mandal, D.; Ghoshal, S. P.

2016-07-01

This paper proposes a novel hybrid optimisation algorithm which combines the recently proposed evolutionary algorithm Backtracking Search Algorithm (BSA) with another widely accepted evolutionary algorithm, namely, Differential Evolution (DE). The proposed algorithm called BSA-DE is employed for the optimal designs of two commonly used analogue circuits, namely Complementary Metal Oxide Semiconductor (CMOS) differential amplifier circuit with current mirror load and CMOS two-stage operational amplifier (op-amp) circuit. BSA has a simple structure that is effective, fast and capable of solving multimodal problems. DE is a stochastic, population-based heuristic approach, having the capability to solve global optimisation problems. In this paper, the transistors' sizes are optimised using the proposed BSA-DE to minimise the areas occupied by the circuits and to improve the performances of the circuits. The simulation results justify the superiority of BSA-DE in global convergence properties and fine tuning ability, and prove it to be a promising candidate for the optimal design of the analogue CMOS amplifier circuits. The simulation results obtained for both the amplifier circuits prove the effectiveness of the proposed BSA-DE-based approach over DE, harmony search (HS), artificial bee colony (ABC) and PSO in terms of convergence speed, design specifications and design parameters of the optimal design of the analogue CMOS amplifier circuits. It is shown that BSA-DE-based design technique for each amplifier circuit yields the least MOS transistor area, and each designed circuit is shown to have the best performance parameters such as gain, power dissipation, etc., as compared with those of other recently reported literature.

Parameter identification of piezoelectric hysteresis model based on improved artificial bee colony algorithm

NASA Astrophysics Data System (ADS)

Wang, Geng; Zhou, Kexin; Zhang, Yeming

2018-04-01

The widely used Bouc-Wen hysteresis model can be utilized to accurately simulate the voltage-displacement curves of piezoelectric actuators. In order to identify the unknown parameters of the Bouc-Wen model, an improved artificial bee colony (IABC) algorithm is proposed in this paper. A guiding strategy for searching the current optimal position of the food source is proposed in the method, which can help balance the local search ability and global exploitation capability. And the formula for the scout bees to search for the food source is modified to increase the convergence speed. Some experiments were conducted to verify the effectiveness of the IABC algorithm. The results show that the identified hysteresis model agreed well with the actual actuator response. Moreover, the identification results were compared with the standard particle swarm optimization (PSO) method, and it can be seen that the search performance in convergence rate of the IABC algorithm is better than that of the standard PSO method.

The modification of hybrid method of ant colony optimization, particle swarm optimization and 3-OPT algorithm in traveling salesman problem

NASA Astrophysics Data System (ADS)

Hertono, G. F.; Ubadah; Handari, B. D.

2018-03-01

The traveling salesman problem (TSP) is a famous problem in finding the shortest tour to visit every vertex exactly once, except the first vertex, given a set of vertices. This paper discusses three modification methods to solve TSP by combining Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO) and 3-Opt Algorithm. The ACO is used to find the solution of TSP, in which the PSO is implemented to find the best value of parameters α and β that are used in ACO.In order to reduce the total of tour length from the feasible solution obtained by ACO, then the 3-Opt will be used. In the first modification, the 3-Opt is used to reduce the total tour length from the feasible solutions obtained at each iteration, meanwhile, as the second modification, 3-Opt is used to reduce the total tour length from the entire solution obtained at every iteration. In the third modification, 3-Opt is used to reduce the total tour length from different solutions obtained at each iteration. Results are tested using 6 benchmark problems taken from TSPLIB by calculating the relative error to the best known solution as well as the running time. Among those modifications, only the second and third modification give satisfactory results except the second one needs more execution time compare to the third modifications.

Counsellor Education as Humanist Colonialism: Seeking Post-Colonial Approaches to Educating Counsellors by Exploring Pathways to an Indigenous Aesthetic

ERIC Educational Resources Information Center

Bowers, Randolph

2008-01-01

This narrative reflection emerged during a time of personally reconnecting with Mi'kmaq First Nation culture and heritage while working in the mainstream roles of counsellor educator and educationalist in Australia. The essay expresses turning points along a path of increasing political and social discomfort with the status quo in counsellor…

Mars Colony: Using Role-Play as a Pedagogical Approach to Teaching Science

ERIC Educational Resources Information Center

Dolenc, Nathan; Wood, Aja; Soldan, Katie; Tai, Robert H.

2016-01-01

In this article, the authors discuss role-play as a pedagogical strategy to engage kindergarten and first-grade students in science and engineering. They present a five-part Mars colony lesson that they developed for a blended class, during which students role-play a space-exploration story that enables them to gain a firsthand perspective of what…

Racist Ordering, Settler Colonialism, and EdTPA: A Participatory Policy Analysis

ERIC Educational Resources Information Center

Tuck, Eve; Gorlewski, Julie

2016-01-01

This article tells the story of an intervention by a collective of teacher educators on New York State's adoption of edTPA. Too often in education policy analysis, issues of race are discussed briefly, if at all. This article argues that attending to constructions of race specific to settler colonialism is an important approach to education policy…

Quantitative spectral light scattering polarimetry for monitoring fractal growth pattern of Bacillus thuringiensis bacterial colonies

NASA Astrophysics Data System (ADS)

Banerjee, Paromita; Soni, Jalpa; Ghosh, Nirmalya; Sengupta, Tapas K.

2013-02-01

It is of considerable current interest to develop various methods which help to understand and quantify the cellular association in growing bacterial colonies and is also important in terms of detection and identification of a bacterial species. A novel approach is used here to probe the morphological structural changes occurring during the growth of the bacterial colony of Bacillus thuringiensis under different environmental conditions (in normal nutrient agar, in presence of glucose - acting as additional nutrient and additional 3mM arsenate as additional toxic material). This approach combines the quantitative Mueller matrix polarimetry to extract intrinsic polarization properties and inverse analysis of the polarization preserving part of the light scattering spectra to determine the fractal parameter H (Hurst exponent) using Born approximation. Interesting differences are observed in the intrinsic polarization parameters and also in the Hurst exponent, which is a measurement of the fractality of a pattern formed by bacteria while growing as a colony. These findings are further confirmed with optical microscopic studies of the same sample and the results indicate a very strong and distinct dependence on the environmental conditions during growth, which can be exploited to quantify different bacterial species and their growth patterns.

Seasonality in communication and collective decision-making in ants.

PubMed

Stroeymeyt, N; Jordan, C; Mayer, G; Hovsepian, S; Giurfa, M; Franks, N R

2014-04-07

The ability of animals to adjust their behaviour according to seasonal changes in their ecology is crucial for their fitness. Eusocial insects display strong collective behavioural seasonality, yet the mechanisms underlying such changes are poorly understood. We show that nest preference by emigrating Temnothorax albipennis ant colonies is influenced by a season-specific modulatory pheromone that may help tune decision-making according to seasonal constraints. The modulatory pheromone triggers aversion towards low-quality nests and enhances colony cohesion in summer and autumn, but not after overwintering-in agreement with reports that field colonies split in spring and reunite in summer. Interestingly, we show that the pheromone acts by downgrading the perceived value of marked nests by informed and naive individuals. This contrasts with theories of collective intelligence, stating that accurate collective decision-making requires independent evaluation of options by individuals. The violation of independence highlighted here was accordingly shown to increase error rate during emigrations. However, this is counterbalanced by enhanced cohesion and the transmission of valuable information through the colony. Our results support recent claims that optimal decisions are not necessarily those that maximize accuracy. Other criteria-such as cohesion or reward rate-may be more relevant in animal decision-making.

Sylvatic plague vaccine and management of prairie dogs

USGS Publications Warehouse

Rocke, Tonie E.

2012-01-01

Scientists at the USGS National Wildlife Health Center (NWHC), in collaboration with colleagues at the University of Wisconsin (UW), have developed a sylvatic plague vaccine that shows great promise in protecting prairie dogs against plague (Mencher and others, 2004; Rocke and others, 2010). Four species of prairie dogs reside in the United States and Canada, and all are highly susceptible to plague and regularly experience outbreaks with devastating losses. Along with habitat loss and poisoning, plague has contributed to a significant historical decline in prairie dog populations. By some estimates, prairie dogs now occupy only 1 to 2 percent of their former range (Proctor and others, 2006), with prairie dog colonies being now much smaller and fragmented than they were historically, making individual colonies more vulnerable to elimination by plague (Antolin and others, 2002). At least one species, the Utah prairie dog (Cynomys parvidens) is listed by the U.S. Fish and Wildlife Service (FWS) as "threatened." Controlling plague is a vital concern for ongoing management and conservation efforts for prairie dogs. Current efforts to halt the spread of plague in prairie dog colonies typically rely on dusting individual prairie dog burrows with pesticides to kill plague-infected fleas. Although flea-control insecticides, such as deltamethrin, are useful in stopping plague outbreaks in these prairie dog colonies, dusting of burrows is labor intensive and time consuming and may affect other insects and arthropods. As an alternative approach, NWHC and UW scientists developed a sylvatic plague vaccine (SPV) for prairie dogs that can be delivered via oral bait. Laboratory studies have shown that consumption of this vaccine-laden bait by different prairie dog species results in significant protection against plague infection that can last for at least 9 months (Rocke and others, 2010; Rocke, unpublished). Work has now shifted to optimizing baits and distribution methods for field delivery of the vaccine. Ultimately, the bait will be formulated in a size and shape that facilitates distribution by plane or overland vehicle. Field studies to assess the safety and efficacy of SPV are being planned. These studies will require involvement from numerous partners, including state and federal land management agencies, tribal organizations, private landowners and non-government agencies.

Groups have a larger cognitive capacity than individuals.

PubMed

Sasaki, Takao; Pratt, Stephen C

2012-10-09

Increasing the number of options can paradoxically lead to worse decisions, a phenomenon known as cognitive overload [1]. This happens when an individual decision-maker attempts to digest information exceeding its processing capacity. Highly integrated groups, such as social insect colonies, make consensus decisions that combine the efforts of many members, suggesting that these groups can overcome individual limitations [2-4]. Here we report that an ant colony choosing a new nest site is less vulnerable to cognitive overload than an isolated ant making this decision on her own. We traced this improvement to differences in individual behavior. In whole colonies, each ant assesses only a small subset of available sites, and the colony combines their efforts to thoroughly explore all options. An isolated ant, on the other hand, must personally assess a larger number of sites to approach the same level of option coverage. By sharing the burden of assessment, the colony avoids overtaxing the abilities of its members. Copyright © 2012 Elsevier Ltd. All rights reserved.

Colonialist Pasts and Afrosurrealist Futures: Decolonizing Race and Doctorhood in Doctor Who.

PubMed

M Asif, Saljooq; Saenz, Cindy

2017-11-29

Originally premiering in 1963, the BBC television series Doctor Who has long been criticized for essentializing colonial scenarios and failing to address issues of race and post-colonial realities. As a white male with the privilege to explore time and space, the titular Doctor stands in contrast to his human companion Martha Jones, a Black woman who represents the first and only main character in the show to be a medical professional of color. The relationship between the Doctor and Martha inherently demands an exploration of the meaning of doctorhood. In studying the ways in which these characters embody the idea of "doctor," we examine how race structures their approach to medicine, heroism, and colonialism. Whereas the Doctor personifies the figure of colonizer and post-colonial white savior, Martha emerges as a radical figure whose doctorhood potentially challenges and dismantles the colonial history of medicine. Through Afrofuturist and Afrosurrealist lenses, Martha represents a potentially subversive figure who offers a visionary medicine rooted in social justice.

Concepts and applications of "natural computing" techniques in de novo drug and peptide design.

PubMed

Hiss, Jan A; Hartenfeller, Markus; Schneider, Gisbert

2010-05-01

Evolutionary algorithms, particle swarm optimization, and ant colony optimization have emerged as robust optimization methods for molecular modeling and peptide design. Such algorithms mimic combinatorial molecule assembly by using molecular fragments as building-blocks for compound construction, and relying on adaptation and emergence of desired pharmacological properties in a population of virtual molecules. Nature-inspired algorithms might be particularly suited for bioisosteric replacement or scaffold-hopping from complex natural products to synthetically more easily accessible compounds that are amenable to optimization by medicinal chemistry. The theory and applications of selected nature-inspired algorithms for drug design are reviewed, together with practical applications and a discussion of their advantages and limitations.

A one-step matrix application method for MALDI mass spectrometry imaging of bacterial colony biofilms.

PubMed

Li, Bin; Comi, Troy J; Si, Tong; Dunham, Sage J B; Sweedler, Jonathan V

2016-11-01

Matrix-assisted laser desorption/ionization imaging of biofilms cultured on agar plates is challenging because of problems related to matrix deposition onto agar. We describe a one-step, spray-based application of a 2,5-dihydroxybenzoic acid solution for direct matrix-assisted laser desorption/ionization imaging of hydrated Bacillus subtilis biofilms on agar. Using both an optimized airbrush and a home-built automatic sprayer, region-specific distributions of signaling metabolites and cannibalistic factors were visualized from B. subtilis cells cultivated on biofilm-promoting medium. The approach provides a homogeneous, relatively dry coating on hydrated samples, improving spot to spot signal repeatability compared with sieved matrix application, and is easily adapted for imaging a range of agar-based biofilms. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Solving the vehicle routing problem by a hybrid meta-heuristic algorithm

NASA Astrophysics Data System (ADS)

Yousefikhoshbakht, Majid; Khorram, Esmaile

2012-08-01

The vehicle routing problem (VRP) is one of the most important combinational optimization problems that has nowadays received much attention because of its real application in industrial and service problems. The VRP involves routing a fleet of vehicles, each of them visiting a set of nodes such that every node is visited by exactly one vehicle only once. So, the objective is to minimize the total distance traveled by all the vehicles. This paper presents a hybrid two-phase algorithm called sweep algorithm (SW) + ant colony system (ACS) for the classical VRP. At the first stage, the VRP is solved by the SW, and at the second stage, the ACS and 3-opt local search are used for improving the solutions. Extensive computational tests on standard instances from the literature confirm the effectiveness of the presented approach.

Connecting the Empire: New Research Perspectives on Infrastructures and the Environment in the (Post)Colonial World.

PubMed

van der Straeten, Jonas; Hasenöhrl, Ute

2016-12-01

In the academic debate on infrastructures in the Global South, there is a broad consensus that (post)colonial legacies present a major challenge for a transition towards more inclusive, sustainable and adapted modes of providing services. Yet, relatively little is known about the emergence and evolution of infrastructures in former colonies. Until a decade ago, most historical studies followed Daniel Headrick's (1981) "tools of empire" thesis, painting-with broad brush strokes-a picture of infrastructures as instruments for advancing the colonial project of exploitation and subordination of non-European peoples and environments. This paper explores new research perspectives beyond this straightforward, 'diffusionist' perspective on technology transfer. In order to do so, it presents and discusses more recent studies which focus on interactive transfer processes as well as mechanisms of appropriation, and which increasingly combine approaches from imperial history, environmental history, and history of technology.There is much to gain from unpacking the changing motives and ideologies behind technology transfer; tracing the often contested and negotiated flows of ideas, technologies and knowledge within multilayered global networks; investigating the manifold ways in which infrastructures reflected and (re)produced colonial spaces and identities; critically reflecting on the utility of large (socio)technical systems (LTS) for the Global South; and approaching infrastructures in the (post)colonial world through entangled histories of technology and the environment. Following David Arnold's (2005) plea for a "more interactive, culturally-nuanced, multi-sited debate" on technology in the non-Western world, the paper offers fresh insights for a broader debate about how infrastructures work within specific parameters of time, place and culture.

Linear antenna array optimization using flower pollination algorithm.

PubMed

Saxena, Prerna; Kothari, Ashwin

2016-01-01

Flower pollination algorithm (FPA) is a new nature-inspired evolutionary algorithm used to solve multi-objective optimization problems. The aim of this paper is to introduce FPA to the electromagnetics and antenna community for the optimization of linear antenna arrays. FPA is applied for the first time to linear array so as to obtain optimized antenna positions in order to achieve an array pattern with minimum side lobe level along with placement of deep nulls in desired directions. Various design examples are presented that illustrate the use of FPA for linear antenna array optimization, and subsequently the results are validated by benchmarking along with results obtained using other state-of-the-art, nature-inspired evolutionary algorithms such as particle swarm optimization, ant colony optimization and cat swarm optimization. The results suggest that in most cases, FPA outperforms the other evolutionary algorithms and at times it yields a similar performance.

Rebel girls? Unplanned pregnancy and colonialism in highlands Papua, Indonesia.

PubMed

Butt, Leslie; Munro, Jenny

2007-01-01

In highlands Papua, Indonesia, rapid social change under a colonial system of governance has created novel sexual opportunities for young indigenous women. Recent scholarship has viewed similar youthful sexual practices that challenge the status quo as expressions of personal agency. By looking at how young women and their families cope with unplanned pregnancies, we suggest that a more viable analytic approach would be to view sexuality, pregnancy and childbirth as a single unit of analysis. From this perspective, young women's experiences are primarily ones of constraint. Case studies offer insights into the ways a political context of colonial domination limits options and choices for young women who have children born out of wedlock. In particular, this paper describes how the 'settler gaze' - omnipresent colonial norms and judgments - creates regulatory effects in the realm of reproduction.

Cultural Foundations of the Idea and Practice of the Teaching Profession in Africa: Indigenous Roots, Colonial Intrusion, and Post-Colonial Reality

ERIC Educational Resources Information Center

Assie-Lumumba, N'Dri T.

2012-01-01

In this article I analyze some of the cultural factors that have determined and influenced the teaching profession and its evolution in African countries. Firstly, I use an historical approach to review conceptual issues on teachers, teaching and learning; secondly, I examine salient features of the idea and practices of teachers and teaching in…

Deviant Constructions: How Governments Preserve Colonial Narratives of Addictions and Poor Mental Health to Intervene into the Lives of Indigenous Children and Families in Canada

ERIC Educational Resources Information Center

de Leeuw, Sarah; Greenwood, Margo; Cameron, Emilie

2010-01-01

Colonial projects in Canada have a long history of violently intervening into the personal lives and social structures of Indigenous peoples. These interventions are associated with elevated rates of addictions and mental health issues among Indigenous peoples. In this paper we employ an indigenized social determinants approach to mental health…

Hybrid protection algorithms based on game theory in multi-domain optical networks

NASA Astrophysics Data System (ADS)

Guo, Lei; Wu, Jingjing; Hou, Weigang; Liu, Yejun; Zhang, Lincong; Li, Hongming

2011-12-01

With the network size increasing, the optical backbone is divided into multiple domains and each domain has its own network operator and management policy. At the same time, the failures in optical network may lead to a huge data loss since each wavelength carries a lot of traffic. Therefore, the survivability in multi-domain optical network is very important. However, existing survivable algorithms can achieve only the unilateral optimization for profit of either users or network operators. Then, they cannot well find the double-win optimal solution with considering economic factors for both users and network operators. Thus, in this paper we develop the multi-domain network model with involving multiple Quality of Service (QoS) parameters. After presenting the link evaluation approach based on fuzzy mathematics, we propose the game model to find the optimal solution to maximize the user's utility, the network operator's utility, and the joint utility of user and network operator. Since the problem of finding double-win optimal solution is NP-complete, we propose two new hybrid protection algorithms, Intra-domain Sub-path Protection (ISP) algorithm and Inter-domain End-to-end Protection (IEP) algorithm. In ISP and IEP, the hybrid protection means that the intelligent algorithm based on Bacterial Colony Optimization (BCO) and the heuristic algorithm are used to solve the survivability in intra-domain routing and inter-domain routing, respectively. Simulation results show that ISP and IEP have the similar comprehensive utility. In addition, ISP has better resource utilization efficiency, lower blocking probability, and higher network operator's utility, while IEP has better user's utility.

Population-Based Ant Colony Optimization for Multivariate Microaggregation

ERIC Educational Resources Information Center

Aksut, Ann Ahu

2013-01-01

Numerous organizations collect and distribute non-aggregate personal data for a variety of different purposes, including demographic and public health research. In these situations, the data distributor is responsible with the protection of the anonymity and personal information of individuals. Microaggregation is one of the most commonly used…

Morphometric differences and fluctuating asymmetry in Melipona subnitida Ducke 1910 (Hymenoptera: Apidae) in different types of housing.

PubMed

Lima, C B S; Nunes, L A; Carvalho, C A L; Ribeiro, M F; Souza, B A; Silva, C S B

2016-01-01

A geometric morphometrics approach was applied to evaluate differences in forewing patterns of the Jandaira bee (Melipona subnitida Ducke). For this, we studied the presence of fluctuating asymmetry (FA) in forewing shape and size of colonies kept in either rational hive boxes or natural tree trunks. We detected significant FA for wing size as well as wing shape independent of the type of housing (rational box or tree trunks), indicating the overall presence of stress during the development of the studied specimens. FA was also significant (p < 0.01) between rational boxes, possibly related to the use of various models of rational boxes used for keeping stingless bees. In addition, a Principal Component Analysis indicated morphometric variation between bee colonies kept in either rational hive boxes or in tree trunks, that may be related to the different origins of the bees: tree trunk colonies were relocated natural colonies while rational box colonies originated from multiplying other colonies. We conclude that adequate measures should be taken to reduce the amount of stress during bee handling by using standard models of rational boxes that cause the least disruption.

Notes from Batavia, the Europeans' graveyard: the nineteenth-century debate on acclimatization in the Dutch East Indies.

PubMed

Pols, Hans

2012-01-01

Since the advent of European colonial expansion, medical theories of acclimatization have been inextricably related to convictions about the possibility and desirability of white settlement in the colonies, and political ideas of colonial governance. Before 1800, acclimatization theories emphasized the inherent flexibility of the human constitution and its ability to adapt to new environments. During the first half of the nineteenth century, European theorists came to highlight the vulnerability of white Europeans in the tropics to disease, degeneration, and death instead. They consequently argued that white settlement in the tropics was impossible and inadvisable. European physicians in the British and French colonies presented similar views. By contrast, their colleagues in the Dutch East Indies remained optimistic. They associated themselves with the colonial European settler community and shared their grievances against autocratic colonial rule. They presented medical theories which related acclimatization to prudent behavior, morality, and proper management of the environment, thereby downplaying the significance of climate and high temperatures. During the following decades, their views on acclimatization were transferred to the Netherlands, where they were deployed as an argument against the cultivation system, the then-current approach of colonial governance, which emphasized the trade of cash crops grown by the indigenous population, severely limited European settlement, and curtailed the rights of Europeans living in the Indies. Throughout the nineteenth century, the influence of climate and the possibility of acclimatization became recurring themes in debates about colonial governance in both the Dutch East Indies and the Netherlands.

Clonogenic colony-forming ability of flow cytometrically isolated hepatic progenitor cells in the murine fetal liver.

PubMed

Taniguchi, H; Kondo, R; Suzuki, A; Zheng, Y W; Takada, Y; Fukunaga, K; Seino, K; Yuzawa, K; Otsuka, M; Fukao, K; Nakauchi, H

2000-01-01

Stem cells are defined as cells having multilineage differentiation potential and self-renewal capability. Hepatic stem cells have aroused considerable interest not only because of their developmental importance but also for their therapeutic potential. However, their presence in the liver has not yet been demonstrated. With the use of a fluorescence-activated cell sorter (FACS) and monoclonal antibodies, we attempted to ascertain whether hepatic stem cells are present in the murine fetal liver. For this purpose, we optimized a cell isolation technique for FACS sorting of fetal liver cells. When isolated CD45 TER119 cells (the non-blood cell fraction in the fetal liver) were tested for their clonogenic colony-forming ability, mechanical dissociation (pipetting) was the most suitable cell isolation technique for FACS sorting. We confirmed that these colonies contained not only cells expressing hepatocyte markers but also cells expressing cholangiocyte markers. To identify hepatic stem cells, studies must focus on CD45TER119- cells in the murine fetal liver.

Bees for development: Brazilian survey reveals how to optimize stingless beekeeping.

PubMed

Jaffé, Rodolfo; Pope, Nathaniel; Torres Carvalho, Airton; Madureira Maia, Ulysses; Blochtein, Betina; de Carvalho, Carlos Alfredo Lopes; Carvalho-Zilse, Gislene Almeida; Freitas, Breno Magalhães; Menezes, Cristiano; Ribeiro, Márcia de Fátima; Venturieri, Giorgio Cristino; Imperatriz-Fonseca, Vera Lucia

2015-01-01

Stingless bees are an important asset to assure plant biodiversity in many natural ecosystems, and fulfill the growing agricultural demand for pollination. However, across developing countries stingless beekeeping remains an essentially informal activity, technical knowledge is scarce, and management practices lack standardization. Here we profited from the large diversity of stingless beekeepers found in Brazil to assess the impact of particular management practices on productivity and economic revenues from the commercialization of stingless bee products. Our study represents the first large-scale effort aiming at optimizing stingless beekeeping for honey/colony production based on quantitative data. Survey data from 251 beekeepers scattered across 20 Brazilian States revealed the influence of specific management practices and other confounding factors over productivity and income indicators. Specifically, our results highlight the importance of teaching beekeepers how to inspect and feed their colonies, how to multiply them and keep track of genetic lineages, how to harvest and preserve the honey, how to use vinegar traps to control infestation by parasitic flies, and how to add value by labeling honey containers. Furthermore, beekeeping experience and the network of known beekeepers were found to be key factors influencing productivity and income. Our work provides clear guidelines to optimize stingless beekeeping and help transform the activity into a powerful tool for sustainable development.

An Ant Colony Optimization algorithm for solving the fixed destination multi-depot multiple traveling salesman problem with non-random parameters

NASA Astrophysics Data System (ADS)

Ramadhani, T.; Hertono, G. F.; Handari, B. D.

2017-07-01

The Multiple Traveling Salesman Problem (MTSP) is the extension of the Traveling Salesman Problem (TSP) in which the shortest routes of m salesmen all of which start and finish in a single city (depot) will be determined. If there is more than one depot and salesmen start from and return to the same depot, then the problem is called Fixed Destination Multi-depot Multiple Traveling Salesman Problem (MMTSP). In this paper, MMTSP will be solved using the Ant Colony Optimization (ACO) algorithm. ACO is a metaheuristic optimization algorithm which is derived from the behavior of ants in finding the shortest route(s) from the anthill to a form of nourishment. In solving the MMTSP, the algorithm is observed with respect to different chosen cities as depots and non-randomly three parameters of MMTSP: m, K, L, those represents the number of salesmen, the fewest cities that must be visited by a salesman, and the most number of cities that can be visited by a salesman, respectively. The implementation is observed with four dataset from TSPLIB. The results show that the different chosen cities as depots and the three parameters of MMTSP, in which m is the most important parameter, affect the solution.

Bees for Development: Brazilian Survey Reveals How to Optimize Stingless Beekeeping

PubMed Central

Jaffé, Rodolfo; Pope, Nathaniel; Carvalho, Airton Torres; Maia, Ulysses Madureira; Blochtein, Betina; de Carvalho, Carlos Alfredo Lopes; Carvalho-Zilse, Gislene Almeida; Freitas, Breno Magalhães; Menezes, Cristiano; de Fátima Ribeiro, Márcia; Venturieri, Giorgio Cristino; Imperatriz-Fonseca, Vera Lucia

2015-01-01

Stingless bees are an important asset to assure plant biodiversity in many natural ecosystems, and fulfill the growing agricultural demand for pollination. However, across developing countries stingless beekeeping remains an essentially informal activity, technical knowledge is scarce, and management practices lack standardization. Here we profited from the large diversity of stingless beekeepers found in Brazil to assess the impact of particular management practices on productivity and economic revenues from the commercialization of stingless bee products. Our study represents the first large-scale effort aiming at optimizing stingless beekeeping for honey/colony production based on quantitative data. Survey data from 251 beekeepers scattered across 20 Brazilian States revealed the influence of specific management practices and other confounding factors over productivity and income indicators. Specifically, our results highlight the importance of teaching beekeepers how to inspect and feed their colonies, how to multiply them and keep track of genetic lineages, how to harvest and preserve the honey, how to use vinegar traps to control infestation by parasitic flies, and how to add value by labeling honey containers. Furthermore, beekeeping experience and the network of known beekeepers were found to be key factors influencing productivity and income. Our work provides clear guidelines to optimize stingless beekeeping and help transform the activity into a powerful tool for sustainable development. PMID:25826402

Optimal reproduction strategies in two species of mound-building termites.

PubMed

Cameron, David A; Ivers, David J; Evans, Theodore A; Myerscough, Mary R

2008-01-01

We formulate a mathematical model for food collection and production of workers and nymphs in 2 species of mound building termites. We maximise the number of nymphs (reproductives) produced by each colony over its lifetime with respect to the proportion of eggs that hatch as nymphs as opposed to workers. The results predict that food storage has a very important influence on the pattern of nymph and worker production. Food storage affects the part of the year that nymph production dominates, whether nymphs and workers are produced at the same time or not, and the existence of a final phase in the colony's life when a very large number of nymphs but no workers are produced.

Optimizing Drone Fertility With Spring Nutritional Supplements to Honey Bee (Hymenoptera: Apidae) Colonies.

PubMed

Rousseau, Andrée; Giovenazzo, Pierre

2016-03-27

Supplemental feeding of honey bee (Apis melliferaL., Hymenoptera: Apidae) colonies in spring is essential for colony buildup in northern apicultural regions. The impact of pollen and syrup feeding on drone production and sperm quality is not well-documented, but may improve fecundation of early-bred queens. We measured the impact of feeding sucrose syrup, and protein supplements to colonies in early spring in eastern Canada. Drones were reared under different nutritional regimes, and mature individuals were then assessed in regard to size, weight, and semen quality (semen volume, sperm count, and viability). Results showed significant increases in drone weight and abdomen size when colonies were fed sucrose and a protein supplement. Colonies receiving no additional nourishment had significantly less semen volume per drone and lower sperm viability. Our study demonstrates that feeding honey bee colonies in spring with sucrose syrup and a protein supplement is important to enhance drone reproductive quality. RÉSUMÉ: L'administration de suppléments alimentaires aux colonies de l'abeille domestique (Apis melliferaL., Hymenoptera: Apidae) au printemps est essentielle pour le bon développement des colonies dans les régions apicoles nordiques. L'impact de la supplémentation des colonies en pollen et en sirop sur la production des faux-bourdons et la qualité du sperme demeure peu documenté mais pourrait résulter en une meilleure fécondation des reines produites tôt en saison. Nous avons mesuré l'impact de la supplémentation en sirop et/ou en supplément de pollen sur les colonies d'abeilles tôt au printemps dans l'est du Canada. Les faux-bourdons ont été élevé sous différents régimes alimentaires et les individus matures ont ensuite été évalués pour leur taille, leur poids ainsi que la qualité de leur sperme (volume de sperme, nombre et viabilité des spermatozoïdes. Les résultats montrent une augmentation significative du poids et de la taille de l'abdomen des faux-bourdons élevés dans les colonies recevant des suppléments de sirop et de protéine. Les faux-bourdons élevés dans les colonies ne recevant aucun supplément alimentaire possédaient les plus petits volumes de sperme ainsi que la plus faible viabilité des spermatozoïdes. Notre étude démontre que la supplémentation alimentaire des colonies en sirop et en protéines au printemps est importante pour favoriser la qualité reproductive des faux-bourdons. © The Authors 2016. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Big data analytics in hyperspectral imaging for detection of microbial colonies on agar plates (Conference Presentation)

NASA Astrophysics Data System (ADS)

Yoon, Seung-Chul; Park, Bosoon; Lawrence, Kurt C.

2017-05-01

Various types of optical imaging techniques measuring light reflectivity and scattering can detect microbial colonies of foodborne pathogens on agar plates. Until recently, these techniques were developed to provide solutions for hypothesis-driven studies, which focused on developing tools and batch/offline machine learning methods with well defined sets of data. These have relatively high accuracy and rapid response time because the tools and methods are often optimized for the collected data. However, they often need to be retrained or recalibrated when new untrained data and/or features are added. A big-data driven technique is more suitable for online learning of new/ambiguous samples and for mining unknown or hidden features. Although big data research in hyperspectral imaging is emerging in remote sensing and many tools and methods have been developed so far in many other applications such as bioinformatics, the tools and methods still need to be evaluated and adjusted in applications where the conventional batch machine learning algorithms were dominant. The primary objective of this study is to evaluate appropriate big data analytic tools and methods for online learning and mining of foodborne pathogens on agar plates. After the tools and methods are successfully identified, they will be applied to rapidly search big color and hyperspectral image data of microbial colonies collected over the past 5 years in house and find the most probable colony or a group of colonies in the collected big data. The meta-data, such as collection time and any unstructured data (e.g. comments), will also be analyzed and presented with output results. The expected results will be novel, big data-driven technology to correctly detect and recognize microbial colonies of various foodborne pathogens on agar plates.

Impact of Salt and Nutrient Content on Biofilm Formation by Vibrio fischeri.

PubMed

Marsden, Anne E; Grudzinski, Kevin; Ondrey, Jakob M; DeLoney-Marino, Cindy R; Visick, Karen L

2017-01-01

Vibrio fischeri, a marine bacterium and symbiont of the Hawaiian bobtail squid Euprymna scolopes, depends on biofilm formation for successful colonization of the squid's symbiotic light organ. Here, we investigated if culture conditions, such as nutrient and salt availability, affect biofilm formation by V. fischeri by testing the formation of wrinkled colonies on solid media. We found that V. fischeri forms colonies with more substantial wrinkling when grown on the nutrient-dense LBS medium containing NaCl relative to those formed on the more nutrient-poor, seawater-salt containing SWT medium. The presence of both tryptone and yeast extract was necessary for the production of "normal" wrinkled colonies; when grown on tryptone alone, the colonies displayed a divoting phenotype and were attached to the agar surface. We also found that the type and concentration of specific seawater salts influenced the timing of biofilm formation. Of the conditions assayed, wrinkled colony formation occurred earliest in LBS(-Tris) media containing 425 mM NaCl, 35 mM MgSO4, and 5 mM CaCl2. Pellicle formation, another measure of biofilm development, was also enhanced in these growth conditions. Therefore, both nutrient and salt availability contribute to V. fischeri biofilm formation. While growth was unaffected, these optimized conditions resulted in increased syp locus expression as measured by a PsypA-lacZ transcriptional reporter. We anticipate these studies will help us understand how the natural environment of V. fischeri affects its ability to form biofilms and, ultimately, colonize E. scolopes.

Longitudinal Effects of Supplemental Forage on the Honey Bee (Apis mellifera) Microbiota and Inter- and Intra-Colony Variability.

PubMed

Rothman, Jason A; Carroll, Mark J; Meikle, William G; Anderson, Kirk E; McFrederick, Quinn S

2018-02-03

Honey bees (Apis mellifera) provide vital pollination services for a variety of agricultural crops around the world and are known to host a consistent core bacterial microbiome. This symbiotic microbial community is essential to many facets of bee health, including likely nutrient acquisition, disease prevention and optimal physiological function. Being that the bee microbiome is likely involved in the digestion of nutrients, we either provided or excluded honey bee colonies from supplemental floral forage before being used for almond pollination. We then used 16S rRNA gene sequencing to examine the effects of forage treatment on the bees' microbial gut communities over four months. In agreement with previous studies, we found that the honey bee gut microbiota is quite stable over time. Similarly, we compared the gut communities of bees from separate colonies and sisters sampled from within the same hive over four months. Surprisingly, we found that the gut microbial communities of individual sisters from the same colony can exhibit as much variation as bees from different colonies. Supplemental floral forage had a subtle effect on the composition of the microbiome during the month of March only, with strains of Gilliamella apicola, Lactobacillus, and Bartonella being less proportionally abundant in bees exposed to forage in the winter. Collectively, our findings show that there is unexpected longitudinal variation within the gut microbial communities of sister honey bees and that supplemental floral forage can subtly alter the microbiome of managed honey bees.

The adaptive significance of phasic colony cycles in army ants.

PubMed

Garnier, Simon; Kronauer, Daniel J C

2017-09-07

Army ants are top arthropod predators in tropical forests around the world. The colonies of many army ant species undergo stereotypical behavioral and reproductive cycles, alternating between brood care and reproductive phases. In the brood care phase, colonies contain a cohort of larvae that are synchronized in their development and have to be fed. In the reproductive phase larvae are absent and oviposition takes place. Despite these colony cycles being a striking feature of army ant biology, their adaptive significance is unclear. Here we use a modeling approach to show that cyclic reproduction is favored under conditions where per capita foraging costs decrease with the number of larvae in a colony ("High Cost of Entry" scenario), while continuous reproduction is favored under conditions where per capita foraging costs increase with the number of larvae ("Resource Exhaustion" scenario). We argue that the former scenario specifically applies to army ants, because large raiding parties are required to overpower prey colonies. However, once raiding is successful it provides abundant food for a large cohort of larvae. The latter scenario, on the other hand, will apply to non-army ants, because in those species local resource depletion will force workers to forage over larger distances to feed large larval cohorts. Our model provides a quantitative framework for understanding the adaptive value of phasic colony cycles in ants. Copyright © 2017 Elsevier Ltd. All rights reserved.

Bus Stops Location and Bus Route Planning Using Mean Shift Clustering and Ant Colony in West Jakarta

NASA Astrophysics Data System (ADS)

Supangat, Kenny; Eko Soelistio, Yustinus

2017-03-01

Traffic Jam has been a daily problem for people in Jakarta which is one of the busiest city in Indonesia up until now. Even though the official government has tried to reduce the impact of traffic issues by developing a new public transportation which takes up a lot of resources and time, it failed to diminish the problem. The actual concern to this problem actually lies in how people move between places in Jakarta where they always using their own vehicle like cars, and motorcycles that fill most of the street in Jakarta. Among much other public transportations that roams the street of Jakarta, Buses is believed to be an efficient transportation that can move many people at once. However, the location of the bus stop is now have moved to the middle of the main road, and its too far for the nearby residence to access to it. This paper proposes an optimal location of optimal bus stops in West Jakarta that is experimentally proven to have a maximal distance of 350 m. The optimal location is estimated by means of mean shift clustering method while the optimal routes are calculated using Ant Colony algorithm. The bus stops locations rate of error is 0.07% with overall route area of 32 km. Based on our experiments, we believe our proposed bus stop plan can be an interesting alternative to reduce traffic congestion in West Jakarta.

Vocalization of echolocation-like pulses for interindividual interaction in horseshoe bats (Rhinolophus ferrumequinum).

PubMed

Kobayasi, Kohta I; Hiryu, Shizuko; Shimozawa, Ryota; Riquimaroux, Hiroshi

2012-11-01

Although much is known about the echolocation of horseshoe bats (Rhinolophus spp.), little is known about the characteristics and function of their communication calls. This study focused on a stereotyped behavior of a bat approaching a companion animal in the colony, and examined their interaction and vocalization during this behavior. The bats emit echolocation-like vocalizations when approaching each other and these vocalizations contain a "buildup" pulse sequence, in which the frequency of the pulse increases gradually to normal echolocation pulse frequencies. The results suggest that the echolocation-like pulses serve an important role in communication within the colony.

Emperor Penguins Breeding on Iceshelves

PubMed Central

Fretwell, Peter T.; Trathan, Phil N.; Wienecke, Barbara; Kooyman, Gerald L.

2014-01-01

We describe a new breeding behaviour discovered in emperor penguins; utilizing satellite and aerial-survey observations four emperor penguin breeding colonies have been recorded as existing on ice-shelves. Emperors have previously been considered as a sea-ice obligate species, with 44 of the 46 colonies located on sea-ice (the other two small colonies are on land). Of the colonies found on ice-shelves, two are newly discovered, and these have been recorded on shelves every season that they have been observed, the other two have been recorded both on ice-shelves and sea-ice in different breeding seasons. We conduct two analyses; the first using synthetic aperture radar data to assess why the largest of the four colonies, for which we have most data, locates sometimes on the shelf and sometimes on the sea-ice, and find that in years where the sea-ice forms late, the colony relocates onto the ice-shelf. The second analysis uses a number of environmental variables to test the habitat marginality of all emperor penguin breeding sites. We find that three of the four colonies reported in this study are in the most northerly, warmest conditions where sea-ice is often sub-optimal. The emperor penguin’s reliance on sea-ice as a breeding platform coupled with recent concerns over changed sea-ice patterns consequent on regional warming, has led to their designation as “near threatened” in the IUCN red list. Current climate models predict that future loss of sea-ice around the Antarctic coastline will negatively impact emperor numbers; recent estimates suggest a halving of the population by 2052. The discovery of this new breeding behaviour at marginal sites could mitigate some of the consequences of sea-ice loss; potential benefits and whether these are permanent or temporary need to be considered and understood before further attempts are made to predict the population trajectory of this iconic species. PMID:24416381

Hyperspectral imaging for detection of non-O157 Shiga-toxin producing Escherichia coli (STEC) serogroups on spread plates of mixed cultures

NASA Astrophysics Data System (ADS)

Yoon, Seung Chul; Windham, William R.; Ladely, Scott; Heitschmidt, Gerald W.; Lawrence, Kurt C.; Park, Bosoon; Narang, Neelam; Cray, William C.

2012-05-01

We investigated the feasibility of visible and near-infrared (VNIR) hyperspectral imaging for rapid presumptive-positive screening of six representative non-O157 Shiga-toxin producing Escherichia coli (STEC) serogroups (O26, O45, O103, O111, O121, and O145) on spread plates of mixed cultures. Although the traditional culture method is still the "gold standard" for presumptive-positive pathogen screening, it is time-consuming, labor-intensive, not effective in testing large amount of food samples, and cannot completely prevent unwanted background microflora from growing together with target microorganisms on agar media. A previous study was performed using the data obtained from pure cultures individually inoculated on spot and/or spread plates in order to develop multivariate classification models differentiating each colony of the six non-O157 STEC serogroups and to optimize the models in terms of parameters. This study dealt with the validation of the trained and optimized models with a test set of new independent samples obtained from colonies on spread plates of mixed cultures. A new validation protocol appropriate to a hyperspectral imaging study for mixed cultures was developed. One imaging experiment with colonies obtained from two serial dilutions was performed. A total of six agar plates were prepared, where O45, O111 and O121 serogroups were inoculated into all six plates and each of O45, O103 and O145 serogroups was added into the mixture of the three common bacterial cultures. The number of colonies grown after 24-h incubation was 331 and the number of pixels associated with the grown colonies was 16,379. The best model found from this validation study was based on pre-processing with standard normal variate and detrending (SNVD), first derivative, spectral smoothing, and k-nearest neighbor classification (kNN, k=3) of scores in the principal component subspace spanned by 6 principal components. The independent testing results showed 95% overall detection accuracy at pixel level and 97% at colony level. The developed model was proven to be still valid even for the independent samples although the size of a test set was small and only one experiment was performed. This study was an important first step in validating and updating multivariate classification models for rapid screening of ground beef samples contaminated by non-O157 STEC pathogens using hyperspectral imaging.

Novel Perspectives on the Characterization of Species-Dependent Optical Signatures of Bacterial Colonies by Digital Holography.

PubMed

Buzalewicz, Igor; Kujawińska, Małgorzata; Krauze, Wojciech; Podbielska, Halina

2016-01-01

The use of light diffraction for the microbiological diagnosis of bacterial colonies was a significant breakthrough with widespread implications for the food industry and clinical practice. We previously confirmed that optical sensors for bacterial colony light diffraction can be used for bacterial identification. This paper is focused on the novel perspectives of this method based on digital in-line holography (DIH), which is able to reconstruct the amplitude and phase properties of examined objects, as well as the amplitude and phase patterns of the optical field scattered/diffracted by the bacterial colony in any chosen observation plane behind the object from single digital hologram. Analysis of the amplitude and phase patterns inside a colony revealed its unique optical properties, which are associated with the internal structure and geometry of the bacterial colony. Moreover, on a computational level, it is possible to select the desired scattered/diffracted pattern within the entire observation volume that exhibits the largest amount of unique, differentiating bacterial features. These properties distinguish this method from the already proposed sensing techniques based on light diffraction/scattering of bacterial colonies. The reconstructed diffraction patterns have a similar spatial distribution as the recorded Fresnel patterns, previously applied for bacterial identification with over 98% accuracy, but they are characterized by both intensity and phase distributions. Our results using digital holography provide new optical discriminators of bacterial species revealed in one single step in form of new optical signatures of bacterial colonies: digital holograms, reconstructed amplitude and phase patterns, as well as diffraction patterns from all observation space, which exhibit species-dependent features. To the best of our knowledge, this is the first report on bacterial colony analysis via digital holography and our study represents an innovative approach to the subject.

Temporal Variation in Honey Production by the Stingless Bee Melipona subnitida (Hymenoptera: Apidae): Long-Term Management Reveals its Potential as a Commercial Species in Northeastern Brazil.

PubMed

Koffler, Sheina; Menezes, Cristiano; Menezes, Paulo Roberto; Kleinert, Astrid de Matos Peixoto; Imperatriz-Fonseca, Vera Lucia; Pope, Nathaniel; Jaffé, Rodolfo

2015-06-01

Even though stingless beekeeping has a great potential as a sustainable development tool, the activity remains essentially informal, technical knowledge is scarce, and management practices lack the sophistication and standardization found in apiculture. Here, we contributed to the further development of stingless beekeeping by investigating the long-term impact of management and climate on honey production and colony survival in the stingless bee Melipona subnitida Ducke (1910). We analyzed a 10-yr record of 155 M. subnitida colonies kept by a commercial honey producer of northeastern Brazil. This constitutes the longest and most accurate record available for a stingless bee. We modeled honey production in relation to time (years), age, management practices (colony division and food supplementation), and climatic factors (temperature and precipitation), and used a model selection approach to identify which factors best explained honey production. We also modeled colony mortality in relation to climatic factors. Although the amount of honey produced by each colony decreased over time, we found that the probability of producing honey increased over the years. Colony divisions decreased honey production, but did not affect honey presence, while supplementary feeding positively affected honey production. In warmer years, the probability of producing honey decreased and the amount of honey produced was lower. In years with lower precipitation, fewer colonies produced honey. In contrast, colony mortality was not affected by climatic factors, and some colonies lived up to nine years, enduring extreme climatic conditions. Our findings provide useful guidelines to improve management and honey production in stingless bees. © The Authors 2015. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Nest and chick survival and colony-site dynamics of least terns in the U.S. Virgin Islands

USGS Publications Warehouse

Lombard, Claudia D.; Collazo, Jaime; McNair, Douglas B.

2010-01-01

We report nest and chick survival and colony-site dynamics of the Least Tern (Sternula antillarum). These results are the first for the Caribbean and were derived with likelihood-based approaches from 4640 nests and 44 chicks fitted with transmitters monitored in 52 colonies at St. Croix, U.S. Virgin Islands, 2003–2006. Managed colonies excluded, overall daily nest survival (±SE) was 0.92 ± 0.03 (period survival = 0.18). Daily nest survival of managed colonies (fenced) was significantly higher (0.97 ± 0.02; period survival = 0.51). Variation in nest survival was best explained by a negative linear trend in daily survival, influenced by year, rain, large colony size, and nesting habitat. Daily nest-survival rates at sandy beaches (0.94 ± 0.02), offshore cays (0.93 ± 0.005), and saltflats (0.91 ± 0.02) did not differ significantly. The period survival of chicks was 0.30 ± 0.11. Estimated fledglings per nest attempt were 0.06. Demographic assessments suggested that higher reproductive rates are required for maintenance (λ ≥ 1). Managed colonies could meet nest-survival thresholds, but complementary measures are needed to increase chick survival. Our findings suggest that management should target sites harboring large colonies because they had higher nest success and higher probability of use in subsequent seasons. The colonies' site dynamics suggested that immigration from other populations is plausible. This possibility relaxes breeding-productivity thresholds and advocates for coordinated conservation among populations on neighboring islands. Estimates of age-specific survival and connectivity are needed for the status of the species to be assessed appropriately and conservation priorities set.

Novel Perspectives on the Characterization of Species-Dependent Optical Signatures of Bacterial Colonies by Digital Holography

PubMed Central

Buzalewicz, Igor; Kujawińska, Małgorzata; Krauze, Wojciech; Podbielska, Halina

2016-01-01

The use of light diffraction for the microbiological diagnosis of bacterial colonies was a significant breakthrough with widespread implications for the food industry and clinical practice. We previously confirmed that optical sensors for bacterial colony light diffraction can be used for bacterial identification. This paper is focused on the novel perspectives of this method based on digital in-line holography (DIH), which is able to reconstruct the amplitude and phase properties of examined objects, as well as the amplitude and phase patterns of the optical field scattered/diffracted by the bacterial colony in any chosen observation plane behind the object from single digital hologram. Analysis of the amplitude and phase patterns inside a colony revealed its unique optical properties, which are associated with the internal structure and geometry of the bacterial colony. Moreover, on a computational level, it is possible to select the desired scattered/diffracted pattern within the entire observation volume that exhibits the largest amount of unique, differentiating bacterial features. These properties distinguish this method from the already proposed sensing techniques based on light diffraction/scattering of bacterial colonies. The reconstructed diffraction patterns have a similar spatial distribution as the recorded Fresnel patterns, previously applied for bacterial identification with over 98% accuracy, but they are characterized by both intensity and phase distributions. Our results using digital holography provide new optical discriminators of bacterial species revealed in one single step in form of new optical signatures of bacterial colonies: digital holograms, reconstructed amplitude and phase patterns, as well as diffraction patterns from all observation space, which exhibit species-dependent features. To the best of our knowledge, this is the first report on bacterial colony analysis via digital holography and our study represents an innovative approach to the subject. PMID:26943121

Chemical fingerprints encode mother–offspring similarity, colony membership, relatedness, and genetic quality in fur seals

PubMed Central

Stoffel, Martin A.; Caspers, Barbara A.; Forcada, Jaume; Giannakara, Athina; Baier, Markus; Eberhart-Phillips, Luke; Müller, Caroline; Hoffman, Joseph I.

2015-01-01

Chemical communication underpins virtually all aspects of vertebrate social life, yet remains poorly understood because of its highly complex mechanistic basis. We therefore used chemical fingerprinting of skin swabs and genetic analysis to explore the chemical cues that may underlie mother–offspring recognition in colonially breeding Antarctic fur seals. By sampling mother–offspring pairs from two different colonies, using a variety of statistical approaches and genotyping a large panel of microsatellite loci, we show that colony membership, mother–offspring similarity, heterozygosity, and genetic relatedness are all chemically encoded. Moreover, chemical similarity between mothers and offspring reflects a combination of genetic and environmental influences, the former partly encoded by substances resembling known pheromones. Our findings reveal the diversity of information contained within chemical fingerprints and have implications for understanding mother–offspring communication, kin recognition, and mate choice. PMID:26261311

Ant colony clustering with fitness perception and pheromone diffusion for community detection in complex networks

NASA Astrophysics Data System (ADS)

Ji, Junzhong; Song, Xiangjing; Liu, Chunnian; Zhang, Xiuzhen

2013-08-01

Community structure detection in complex networks has been intensively investigated in recent years. In this paper, we propose an adaptive approach based on ant colony clustering to discover communities in a complex network. The focus of the method is the clustering process of an ant colony in a virtual grid, where each ant represents a node in the complex network. During the ant colony search, the method uses a new fitness function to percept local environment and employs a pheromone diffusion model as a global information feedback mechanism to realize information exchange among ants. A significant advantage of our method is that the locations in the grid environment and the connections of the complex network structure are simultaneously taken into account in ants moving. Experimental results on computer-generated and real-world networks show the capability of our method to successfully detect community structures.

Amplification of Emerging Viruses in a Bat Colony

PubMed Central

Drexler, Jan Felix; Corman, Victor Max; Wegner, Tom; Tateno, Adriana Fumie; Zerbinati, Rodrigo Melim; Gloza-Rausch, Florian; Seebens, Antje; Müller, Marcel A.

2011-01-01

Bats host noteworthy viral pathogens, including coronaviruses, astroviruses, and adenoviruses. Knowledge on the ecology of reservoir-borne viruses is critical for preventive approaches against zoonotic epidemics. We studied a maternity colony of Myotis myotis bats in the attic of a private house in a suburban neighborhood in Rhineland-Palatinate, Germany, during 2008, 2009, and 2010. One coronavirus, 6 astroviruses, and 1 novel adenovirus were identified and monitored quantitatively. Strong and specific amplification of RNA viruses, but not of DNA viruses, occurred during colony formation and after parturition. The breeding success of the colony was significantly better in 2010 than in 2008, in spite of stronger amplification of coronaviruses and astroviruses in 2010, suggesting that these viruses had little pathogenic influence on bats. However, the general correlation of virus and bat population dynamics suggests that bats control infections similar to other mammals and that they may well experience epidemics of viruses under certain circumstances. PMID:21392436

Granulocyte colony-stimulating factor receptor signaling in severe congenital neutropenia, chronic neutrophilic leukemia, and related malignancies.

PubMed

Dwivedi, Pankaj; Greis, Kenneth D

2017-02-01

Granulocyte colony-stimulating factor is a hematopoietic cytokine that stimulates neutrophil production and hematopoietic stem cell mobilization by initiating the dimerization of homodimeric granulocyte colony-stimulating factor receptor. Different mutations of CSF3R have been linked to a unique spectrum of myeloid disorders and related malignancies. Myeloid disorders caused by the CSF3R mutations include severe congenital neutropenia, chronic neutrophilic leukemia, and atypical chronic myeloid leukemia. In this review, we provide an analysis of granulocyte colony-stimulating factor receptor, various mutations, and their roles in the severe congenital neutropenia, chronic neutrophilic leukemia, and malignant transformation, as well as the clinical implications and some perspective on approaches that could expand our knowledge with respect to the normal signaling mechanisms and those associated with mutations in the receptor. Copyright © 2016 ISEH - International Society for Experimental Hematology. Published by Elsevier Inc. All rights reserved.

A total extinction confidently hoped for: the destruction of Cape San society under Dutch colonial rule, 1700-1795.

PubMed

Adhikari, Mohamed

2010-01-01

San (Bushman) society in the Cape Colony was almost completely annihilated during the eighteenth and nineteenth centuries as a result of land confiscation, massacre, forced labour and cultural suppression that accompanied colonial rule. Whereas similar obliterations of indigenous peoples in other parts of the world have resulted in major public controversies and heated debate amongst academics about the genocidal nature of these episodes, in South Africa the issue has effectively been ignored aside from passing, often polemical, references to it as genocide. Even recent studies that have approached the mass killing of the Cape San with sensitivity and insight do not address it as a case of genocide. This article sets out to redress this imbalance in part by analysing the dynamic of frontier conflict between San and settler under Dutch colonial rule as genocide. It demonstrates both the exterminatory intent underlying settler violence as well as the complicity of a weak colonial state in these depredations, including its sanctioning of the root-and-branch eradication of the San.

Honeybee Colony Vibrational Measurements to Highlight the Brood Cycle

PubMed Central

Bencsik, Martin; Le Conte, Yves; Reyes, Maritza; Pioz, Maryline; Whittaker, David; Crauser, Didier; Simon Delso, Noa; Newton, Michael I.

2015-01-01

Insect pollination is of great importance to crop production worldwide and honey bees are amongst its chief facilitators. Because of the decline of managed colonies, the use of sensor technology is growing in popularity and it is of interest to develop new methods which can more accurately and less invasively assess honey bee colony status. Our approach is to use accelerometers to measure vibrations in order to provide information on colony activity and development. The accelerometers provide amplitude and frequency information which is recorded every three minutes and analysed for night time only. Vibrational data were validated by comparison to visual inspection data, particularly the brood development. We show a strong correlation between vibrational amplitude data and the brood cycle in the vicinity of the sensor. We have further explored the minimum data that is required, when frequency information is also included, to accurately predict the current point in the brood cycle. Such a technique should enable beekeepers to reduce the frequency with which visual inspections are required, reducing the stress this places on the colony and saving the beekeeper time. PMID:26580393

A low-disturbance capture technique for ground-nesting Double-crested Cormorants (Phalacrocorax auritus)

USGS Publications Warehouse

Courtot, Karen; Roby, Daniel D.; Kerr, Lauren H.; Lyons, Donald E.; Adkins, Jessica Y.

2016-01-01

Capturing breeding adults of colonially nesting species can entail risks of nest failure and even colony abandonment, especially in species that react strongly to human disturbance. A low-disturbance technique for capturing specific adult Double-crested Cormorants (Phalacrocorax auritus) at a ground-nesting colony was developed to reduce these risks and is described here. Nesting habitat enhancement was used to attract Doublecrested Cormorants to nest adjacent to above-ground tunnels constructed so that researchers could capture birds by hand. Using this technique, Double-crested Cormorants (n = 87) were captured during the incubation and chick-rearing stages of the nesting cycle. Unlike alternative capture techniques, this approach allowed targeting of specific individuals for capture and recapture, minimized local disturbance, and eliminated colony-wide disturbances. The tunnel-based system presented here could be adapted to capture adults or to access the nest contents of other ground-nesting colonial species that are inclined to nest in areas of enhanced nesting habitat and adapt to anthropogenic structures in their nesting area. This system would be particularly beneficial for other wary and easily disturbed species.

Bringing home the trash: do colony-based differences in foraging distribution lead to increased plastic ingestion in Laysan albatrosses?

PubMed

Young, Lindsay C; Vanderlip, Cynthia; Duffy, David C; Afanasyev, Vsevolod; Shaffer, Scott A

2009-10-28

When searching for prey, animals should maximize energetic gain, while minimizing energy expenditure by altering their movements relative to prey availability. However, with increasing amounts of marine debris, what once may have been 'optimal' foraging strategies for top marine predators, are leading to sub-optimal diets comprised in large part of plastic. Indeed, the highly vagile Laysan albatross (Phoebastria immutabilis) which forages throughout the North Pacific, are well known for their tendency to ingest plastic. Here we examine whether Laysan albatrosses nesting on Kure Atoll and Oahu Island, 2,150 km apart, experience different levels of plastic ingestion. Twenty two geolocators were deployed on breeding adults for up to two years. Regurgitated boluses of undigestable material were also collected from chicks at each site to compare the amount of plastic vs. natural foods. Chicks from Kure Atoll were fed almost ten times the amount of plastic compared to chicks from Oahu despite boluses from both colonies having similar amounts of natural food. Tracking data indicated that adults from either colony did not have core overlapping distributions during the early half of the breeding period and that adults from Kure had a greater overlap with the putative range of the Western Garbage Patch corroborating our observation of higher plastic loads at this colony. At-sea distributions also varied throughout the year suggesting that Laysan albatrosses either adjusted their foraging behavior according to constraints on time away from the nest or to variation in resources. However, in the non-breeding season, distributional overlap was greater indicating that the energy required to reach the foraging grounds was less important than the total energy available. These results demonstrate how a marine predator that is not dispersal limited alters its foraging strategy throughout the reproductive cycle to maximize energetic gain and how this has led to differences in plastic ingestion.

Honey bee (Apis mellifera) nurses do not consume pollens based on their nutritional quality

USDA-ARS?s Scientific Manuscript database

Honey bees (Apis mellifera) consume a variety of pollens to meet the majority of their requirements for protein and lipids. Recent work indicates that at both the colony and individual levels, honey bees prefer diets that reflect the proper ratio of nutrients necessary for optimal survival and homeo...

QSRR modeling for diverse drugs using different feature selection methods coupled with linear and nonlinear regressions.

PubMed

Goodarzi, Mohammad; Jensen, Richard; Vander Heyden, Yvan

2012-12-01

A Quantitative Structure-Retention Relationship (QSRR) is proposed to estimate the chromatographic retention of 83 diverse drugs on a Unisphere poly butadiene (PBD) column, using isocratic elutions at pH 11.7. Previous work has generated QSRR models for them using Classification And Regression Trees (CART). In this work, Ant Colony Optimization is used as a feature selection method to find the best molecular descriptors from a large pool. In addition, several other selection methods have been applied, such as Genetic Algorithms, Stepwise Regression and the Relief method, not only to evaluate Ant Colony Optimization as a feature selection method but also to investigate its ability to find the important descriptors in QSRR. Multiple Linear Regression (MLR) and Support Vector Machines (SVMs) were applied as linear and nonlinear regression methods, respectively, giving excellent correlation between the experimental, i.e. extrapolated to a mobile phase consisting of pure water, and predicted logarithms of the retention factors of the drugs (logk(w)). The overall best model was the SVM one built using descriptors selected by ACO. Copyright © 2012 Elsevier B.V. All rights reserved.

Dynamic routing and spectrum assignment based on multilayer virtual topology and ant colony optimization in elastic software-defined optical networks

NASA Astrophysics Data System (ADS)

Wang, Fu; Liu, Bo; Zhang, Lijia; Zhang, Qi; Tian, Qinghua; Tian, Feng; Rao, Lan; Xin, Xiangjun

2017-07-01

Elastic software-defined optical networks greatly improve the flexibility of the optical switching network while it has brought challenges to the routing and spectrum assignment (RSA). A multilayer virtual topology model is proposed to solve RSA problems. Two RSA algorithms based on the virtual topology are proposed, which are the ant colony optimization (ACO) algorithm of minimum consecutiveness loss and the ACO algorithm of maximum spectrum consecutiveness. Due to the computing power of the control layer in the software-defined network, the routing algorithm avoids the frequent link-state information between routers. Based on the effect of the spectrum consecutiveness loss on the pheromone in the ACO, the path and spectrum of the minimal impact on the network are selected for the service request. The proposed algorithms have been compared with other algorithms. The results show that the proposed algorithms can reduce the blocking rate by at least 5% and perform better in spectrum efficiency. Moreover, the proposed algorithms can effectively decrease spectrum fragmentation and enhance available spectrum consecutiveness.

An ant colony optimization based feature selection for web page classification.

PubMed

Saraç, Esra; Özel, Selma Ayşe

2014-01-01

The increased popularity of the web has caused the inclusion of huge amount of information to the web, and as a result of this explosive information growth, automated web page classification systems are needed to improve search engines' performance. Web pages have a large number of features such as HTML/XML tags, URLs, hyperlinks, and text contents that should be considered during an automated classification process. The aim of this study is to reduce the number of features to be used to improve runtime and accuracy of the classification of web pages. In this study, we used an ant colony optimization (ACO) algorithm to select the best features, and then we applied the well-known C4.5, naive Bayes, and k nearest neighbor classifiers to assign class labels to web pages. We used the WebKB and Conference datasets in our experiments, and we showed that using the ACO for feature selection improves both accuracy and runtime performance of classification. We also showed that the proposed ACO based algorithm can select better features with respect to the well-known information gain and chi square feature selection methods.

Variation in the form of Pavlovian conditioned approach behavior among outbred male Sprague-Dawley rats from different vendors and colonies: sign-tracking vs. goal-tracking.

PubMed

Fitzpatrick, Christopher J; Gopalakrishnan, Shyam; Cogan, Elizabeth S; Yager, Lindsay M; Meyer, Paul J; Lovic, Vedran; Saunders, Benjamin T; Parker, Clarissa C; Gonzales, Natalia M; Aryee, Emmanuel; Flagel, Shelly B; Palmer, Abraham A; Robinson, Terry E; Morrow, Jonathan D

2013-01-01

Even when trained under exactly the same conditions outbred male Sprague-Dawley (SD) rats vary in the form of the Pavlovian conditioned approach response (CR) they acquire. The form of the CR (i.e. sign-tracking vs. goal-tracking) predicts to what degree individuals attribute incentive salience to cues associated with food or drugs. However, we have noticed variation in the incidence of these two phenotypes in rats obtained from different vendors. In this study, we quantified sign- and goal-tracking behavior in a reasonably large sample of SD rats obtained from two vendors (Harlan or Charles River), as well as from individual colonies operated by both vendors. Our sample of rats acquired from Harlan had, on average, more sign-trackers than goal-trackers, and vice versa for our sample of rats acquired from Charles River. Furthermore, there were significant differences among colonies of the same vendor. Although it is impossible to rule out environmental variables, SD rats at different vendors and barriers may have reduced phenotypic heterogeneity as a result of genetic variables, such as random genetic drift or population bottlenecks. Consistent with this hypothesis, we identified marked population structure among colonies from Harlan. Therefore, despite sharing the same name, investigators should be aware that important genetic and phenotypic differences exist among SD rats from different vendors or even from different colonies of the same vendor. If used judiciously this can be an asset to experimental design, but it can also be a pitfall for those unaware of the issue.

Recourse-based facility-location problems in hybrid uncertain environment.

PubMed

Wang, Shuming; Watada, Junzo; Pedrycz, Witold

2010-08-01

The objective of this paper is to study facility-location problems in the presence of a hybrid uncertain environment involving both randomness and fuzziness. A two-stage fuzzy-random facility-location model with recourse (FR-FLMR) is developed in which both the demands and costs are assumed to be fuzzy-random variables. The bounds of the optimal objective value of the two-stage FR-FLMR are derived. As, in general, the fuzzy-random parameters of the FR-FLMR can be regarded as continuous fuzzy-random variables with an infinite number of realizations, the computation of the recourse requires solving infinite second-stage programming problems. Owing to this requirement, the recourse function cannot be determined analytically, and, hence, the model cannot benefit from the use of techniques of classical mathematical programming. In order to solve the location problems of this nature, we first develop a technique of fuzzy-random simulation to compute the recourse function. The convergence of such simulation scenarios is discussed. In the sequel, we propose a hybrid mutation-based binary ant-colony optimization (MBACO) approach to the two-stage FR-FLMR, which comprises the fuzzy-random simulation and the simplex algorithm. A numerical experiment illustrates the application of the hybrid MBACO algorithm. The comparison shows that the hybrid MBACO finds better solutions than the one using other discrete metaheuristic algorithms, such as binary particle-swarm optimization, genetic algorithm, and tabu search.

Comparative analysis of quantitative methodologies for Vibrionaceae biofilms.

PubMed

Chavez-Dozal, Alba A; Nourabadi, Neda; Erken, Martina; McDougald, Diane; Nishiguchi, Michele K

2016-11-01

Multiple symbiotic and free-living Vibrio spp. grow as a form of microbial community known as a biofilm. In the laboratory, methods to quantify Vibrio biofilm mass include crystal violet staining, direct colony-forming unit (CFU) counting, dry biofilm cell mass measurement, and observation of development of wrinkled colonies. Another approach for bacterial biofilms also involves the use of tetrazolium (XTT) assays (used widely in studies of fungi) that are an appropriate measure of metabolic activity and vitality of cells within the biofilm matrix. This study systematically tested five techniques, among which the XTT assay and wrinkled colony measurement provided the most reproducible, accurate, and efficient methods for the quantitative estimation of Vibrionaceae biofilms.

A Modified Artificial Bee Colony Algorithm for p-Center Problems

PubMed Central

Yurtkuran, Alkın

2014-01-01

The objective of the p-center problem is to locate p-centers on a network such that the maximum of the distances from each node to its nearest center is minimized. The artificial bee colony algorithm is a swarm-based meta-heuristic algorithm that mimics the foraging behavior of honey bee colonies. This study proposes a modified ABC algorithm that benefits from a variety of search strategies to balance exploration and exploitation. Moreover, random key-based coding schemes are used to solve the p-center problem effectively. The proposed algorithm is compared to state-of-the-art techniques using different benchmark problems, and computational results reveal that the proposed approach is very efficient. PMID:24616648

Periodic Colony Formation of Bacteria Due to their Cell Reproduction and Movement

NASA Astrophysics Data System (ADS)

Itoh, H.; Wakita, J.; Watanabe, K.; Matsuyama, T.; Matsushita, M.

We have experimentally investigated periodic pattern formation produced by bacterial species Proteus mirabilis, which forms concentric-ring-like colonies by repeating migration and rest alternately on the surface of a solid agar medium. We distinguish three phases (initial lag phase, the following migration and consolidation phases that appear alternately) for the colony growth. Here we mainly used physical approaches in order to try to understand the formation of concentric-ring-like colonies, such as cutting the part of a colony during its growth. Global chemical signals governing the colony formation from the center were not found. We also checked phase entrainment quantitatively by letting two colonies collide with each other and confirmed that it does not take place in macroscopic scales. When we cut a colony just behind the migrating front shortly after the migration started, the migration ended earlier and the following consolidation lasted longer. However, the following cycles were not influenced by the cut, i.e., the following migration and consolidation phases were both found to return normal. The cut results in the stop of supply of cell population to the migrating front by internal waves. In fact the cell population on the new terrace during the first migration after the cut was less than that without cut. Furthermore, the cell population density was found to be recovered to the ordinary value by the end of the consolidation. All these experimental results suggest that the most important factor for the repetition of migration and consolidation phases is the cell population density.

Biologically-inspired approaches for self-organization, adaptation, and collaboration of heterogeneous autonomous systems

NASA Astrophysics Data System (ADS)

Steinberg, Marc

2011-06-01

This paper presents a selective survey of theoretical and experimental progress in the development of biologicallyinspired approaches for complex surveillance and reconnaissance problems with multiple, heterogeneous autonomous systems. The focus is on approaches that may address ISR problems that can quickly become mathematically intractable or otherwise impractical to implement using traditional optimization techniques as the size and complexity of the problem is increased. These problems require dealing with complex spatiotemporal objectives and constraints at a variety of levels from motion planning to task allocation. There is also a need to ensure solutions are reliable and robust to uncertainty and communications limitations. First, the paper will provide a short introduction to the current state of relevant biological research as relates to collective animal behavior. Second, the paper will describe research on largely decentralized, reactive, or swarm approaches that have been inspired by biological phenomena such as schools of fish, flocks of birds, ant colonies, and insect swarms. Next, the paper will discuss approaches towards more complex organizational and cooperative mechanisms in team and coalition behaviors in order to provide mission coverage of large, complex areas. Relevant team behavior may be derived from recent advances in understanding of the social and cooperative behaviors used for collaboration by tens of animals with higher-level cognitive abilities such as mammals and birds. Finally, the paper will briefly discuss challenges involved in user interaction with these types of systems.

Combining noninvasive genetics and a new mammalian sex-linked marker provides new tools to investigate population size, structure and individual behaviour: An application to bats.

PubMed

Zarzoso-Lacoste, Diane; Jan, Pierre-Loup; Lehnen, Lisa; Girard, Thomas; Besnard, Anne-Laure; Puechmaille, Sebastien J; Petit, Eric J

2018-03-01

Monitoring wild populations is crucial for their effective management. Noninvasive genetic methods provide robust data from individual free-ranging animals, which can be used in capture-mark-recapture (CMR) models to estimate demographic parameters without capturing or disturbing them. However, sex- and status-specific behaviour, which may lead to differences in detection probabilities, is rarely considered in monitoring. Here, we investigated population size, sex ratio, sex- and status-related behaviour in 19 Rhinolophus hipposideros maternity colonies (Northern France) with a noninvasive genetic CMR approach (using faeces) combined with parentage assignments. The use of the DDX3X/Y-Mam sexual marker designed in this study, which shows inter- and intrachromosomal length polymorphism across placental mammals, together with eight polymorphic microsatellite markers, produced high-quality genetic data with limited genotyping errors and allowed us to reliably distinguish different categories of individuals (males, reproductive and nonreproductive females) and to estimate population sizes. We showed that visual counts represent well-adult female numbers and that population composition in maternity colonies changes dynamically during the summer. Before parturition, colonies mainly harbour pregnant and nonpregnant females with a few visiting males, whereas after parturition, colonies are mainly composed of mothers and their offspring with a few visiting nonmothers and males. Our approach gives deeper insight into sex- and status-specific behaviour, a prerequisite for understanding population dynamics and developing effective monitoring and management strategies. Provided sufficient samples can be obtained, this approach can be readily applied to a wide range of species. © 2017 John Wiley & Sons Ltd.

The coral reefs optimization algorithm: a novel metaheuristic for efficiently solving optimization problems.

PubMed

Salcedo-Sanz, S; Del Ser, J; Landa-Torres, I; Gil-López, S; Portilla-Figueras, J A

2014-01-01

This paper presents a novel bioinspired algorithm to tackle complex optimization problems: the coral reefs optimization (CRO) algorithm. The CRO algorithm artificially simulates a coral reef, where different corals (namely, solutions to the optimization problem considered) grow and reproduce in coral colonies, fighting by choking out other corals for space in the reef. This fight for space, along with the specific characteristics of the corals' reproduction, produces a robust metaheuristic algorithm shown to be powerful for solving hard optimization problems. In this research the CRO algorithm is tested in several continuous and discrete benchmark problems, as well as in practical application scenarios (i.e., optimum mobile network deployment and off-shore wind farm design). The obtained results confirm the excellent performance of the proposed algorithm and open line of research for further application of the algorithm to real-world problems.

The Coral Reefs Optimization Algorithm: A Novel Metaheuristic for Efficiently Solving Optimization Problems

PubMed Central

Salcedo-Sanz, S.; Del Ser, J.; Landa-Torres, I.; Gil-López, S.; Portilla-Figueras, J. A.

2014-01-01

This paper presents a novel bioinspired algorithm to tackle complex optimization problems: the coral reefs optimization (CRO) algorithm. The CRO algorithm artificially simulates a coral reef, where different corals (namely, solutions to the optimization problem considered) grow and reproduce in coral colonies, fighting by choking out other corals for space in the reef. This fight for space, along with the specific characteristics of the corals' reproduction, produces a robust metaheuristic algorithm shown to be powerful for solving hard optimization problems. In this research the CRO algorithm is tested in several continuous and discrete benchmark problems, as well as in practical application scenarios (i.e., optimum mobile network deployment and off-shore wind farm design). The obtained results confirm the excellent performance of the proposed algorithm and open line of research for further application of the algorithm to real-world problems. PMID:25147860

Management Approaches for Water Quality Enhancement at Whitney Point and East Sidney Lake, New York

DTIC Science & Technology

1990-08-01

flos- aguae Anabaena sp. (colonial blue-greens) 8-16-88 Aphanizomenon flos- aguae Anabaena planctonica Coccoid sp. (colonial blue-greens...Goelosphaeriui sp. Microcystis aerug~inosa Comphosphaeria sp. 9-9-88 Aphanizomenon flos- aguae Oscillatoria sp. Melosira spp. Table 9 Dominant Phytoplankton Species...study (Kennedy et al. 1988). Sampling period was initiated in early April, 1988 and completed in early September, 1988. In situ variables, measured at

Promotion of human early embryonic development and blastocyst outgrowth in vitro using autocrine/paracrine growth factors.

PubMed

Kawamura, Kazuhiro; Chen, Yuan; Shu, Yimin; Cheng, Yuan; Qiao, Jie; Behr, Barry; Pera, Renee A Reijo; Hsueh, Aaron J W

2012-01-01

Studies using animal models demonstrated the importance of autocrine/paracrine factors secreted by preimplantation embryos and reproductive tracts for embryonic development and implantation. Although in vitro fertilization-embryo transfer (IVF-ET) is an established procedure, there is no evidence that present culture conditions are optimal for human early embryonic development. In this study, key polypeptide ligands known to be important for early embryonic development in animal models were tested for their ability to improve human early embryo development and blastocyst outgrowth in vitro. We confirmed the expression of key ligand/receptor pairs in cleavage embryos derived from discarded human tri-pronuclear zygotes and in human endometrium. Combined treatment with key embryonic growth factors (brain-derived neurotrophic factor, colony-stimulating factor, epidermal growth factor, granulocyte macrophage colony-stimulating factor, insulin-like growth factor-1, glial cell-line derived neurotrophic factor, and artemin) in serum-free media promoted >2.5-fold the development of tri-pronuclear zygotes to blastocysts. For normally fertilized embryos, day 3 surplus embryos cultured individually with the key growth factors showed >3-fold increases in the development of 6-8 cell stage embryos to blastocysts and >7-fold increase in the proportion of high quality blastocysts based on Gardner's criteria. Growth factor treatment also led to a 2-fold promotion of blastocyst outgrowth in vitro when day 7 surplus hatching blastocysts were used. When failed-to-be-fertilized oocytes were used to perform somatic cell nuclear transfer (SCNT) using fibroblasts as donor karyoplasts, inclusion of growth factors increased the progression of reconstructed SCNT embryos to >4-cell stage embryos. Growth factor supplementation of serum-free cultures could promote optimal early embryonic development and implantation in IVF-ET and SCNT procedures. This approach is valuable for infertility treatment and future derivation of patient-specific embryonic stem cells.

Propulsion and Power Rapid Response Research and Development (R&D) Support. Task Order 0004: Advanced Propulsion Fuels R&D, Subtask: Optimization of Lipid Production and Processing of Microalgae for the Development of Biofuels

DTIC Science & Technology

2013-02-01

Purified cultures are tested for optimized production under heterotrophic conditions with several organic carbon sources like beet and sorghum juice using ...Moreover, AFRL support sponsored the Master’s in Chemical Engineering project titled “Cost Analysis Of Local Bio- Products Processing Plant Using ...unlimited. 2.5 Screening for High Lipid Production Mutants Procedure: A selection of 84 single colony cultures was analyzed in this phase using the

Hybrid Optimization of Object-Based Classification in High-Resolution Images Using Continous ANT Colony Algorithm with Emphasis on Building Detection

NASA Astrophysics Data System (ADS)

Tamimi, E.; Ebadi, H.; Kiani, A.

2017-09-01

Automatic building detection from High Spatial Resolution (HSR) images is one of the most important issues in Remote Sensing (RS). Due to the limited number of spectral bands in HSR images, using other features will lead to improve accuracy. By adding these features, the presence probability of dependent features will be increased, which leads to accuracy reduction. In addition, some parameters should be determined in Support Vector Machine (SVM) classification. Therefore, it is necessary to simultaneously determine classification parameters and select independent features according to image type. Optimization algorithm is an efficient method to solve this problem. On the other hand, pixel-based classification faces several challenges such as producing salt-paper results and high computational time in high dimensional data. Hence, in this paper, a novel method is proposed to optimize object-based SVM classification by applying continuous Ant Colony Optimization (ACO) algorithm. The advantages of the proposed method are relatively high automation level, independency of image scene and type, post processing reduction for building edge reconstruction and accuracy improvement. The proposed method was evaluated by pixel-based SVM and Random Forest (RF) classification in terms of accuracy. In comparison with optimized pixel-based SVM classification, the results showed that the proposed method improved quality factor and overall accuracy by 17% and 10%, respectively. Also, in the proposed method, Kappa coefficient was improved by 6% rather than RF classification. Time processing of the proposed method was relatively low because of unit of image analysis (image object). These showed the superiority of the proposed method in terms of time and accuracy.

Product Mix Selection Using AN Evolutionary Technique

NASA Astrophysics Data System (ADS)

Tsoulos, Ioannis G.; Vasant, Pandian

2009-08-01

This paper proposes an evolutionary technique for the solution of a real—life industrial problem and particular for the product mix selection problem. The evolutionary technique is a combination of a genetic algorithm that preserves the feasibility of the trial solutions with penalties and some local optimization method. The goal of this paper has been achieved in finding the best near optimal solution for the profit fitness function respect to vagueness factor and level of satisfaction. The findings of the profit values will be very useful for the decision makers in the industrial engineering sector for the implementation purpose. It's possible to improve the solutions obtained in this study by employing other meta-heuristic methods such as simulated annealing, tabu Search, ant colony optimization, particle swarm optimization and artificial immune systems.

Are there optimal densities for prairie birds?

USGS Publications Warehouse

Skagen, S.K.; Adams, A.A.Y.

2010-01-01

The major forces of food and predation shape fitness-enhancing decisions of birds at all stages of their life cycles. During the breeding season, birds can minimize nest loss due to predation by selecting sites with a lower probability of predation. To understand the environmental and social aspects and consequences of breedingsite selection in prairie birds, we explored variation in nest-survival patterns of the Lark Bunting (Calamospiza melanocorys) in the shortgrass prairie region of North America. Over four breeding seasons, we documented the survival of 405 nests, conducted 60 surveys to estimate bird densities, and measured several vegetative features to describe habitat structure in 24 randomly selected study plots. Nest survival varied with the buntings' density as described by a quadratic polynomial, increasing with density below 1.5 birds ha-1 and decreasing with density between 1.5 and 3 birds ha-1, suggesting that an optimal range of densities favors reproductive success of the Lark Bunting, which nests semi-colonially. Nest survival also increased with increasing vegetation structure of study plots and varied with age of the nest, increasing during early incubation and late in the nestling stage and declining slightly from mid-incubation to the middle of the nestling period. The existence of an optimal range of densities in this semi-colonial species can be elucidated by the "commodity-selection hypothesis" at low densities and density dependence at high densities. ?? The Cooper Ornithological Society 2010.

Applying ant colony optimization metaheuristic to solve forest transportation planning problems with side constraints

Treesearch

Marco A. Contreras; Woodam Chung; Greg Jones

2008-01-01

Forest transportation planning problems (FTPP) have evolved from considering only the financial aspects of timber management to more holistic problems that also consider the environmental impacts of roads. These additional requirements have introduced side constraints, making FTPP larger and more complex. Mixed-integer programming (MIP) has been used to solve FTPP, but...

Adaptable Learning Pathway Generation with Ant Colony Optimization

ERIC Educational Resources Information Center

Wong, Lung-Hsiang; Looi, Chee-Kit

2009-01-01

One of the new major directions in research on web-based educational systems is the notion of adaptability: the educational system adapts itself to the learning profile, preferences and ability of the student. In this paper, we look into the issues of providing adaptability with respect to learning pathways. We explore the state of the art with…

Targeted Therapy Combined with Immune Modulation Using Gold Nanoparticles for Treating Metastatic Colorectal Cancer

DTIC Science & Technology

2017-09-01

collaborator, Dr. Luke Dow. We bred these pairs of mice to create a colony of transgenic mice, and continue to breed them as needed. When experimental...colony of transgenic mice. When experimental mice are 8 weeks of age, they are treated with 4-hydroxytamofixen (4OHT) and put on continuous doxycycline...and human cells and transgenic mouse models. These experiments will determine the effectiveness of this approach and, if successful, will lead to

Multi-scale imaging and informatics pipeline for in situ pluripotent stem cell analysis.

PubMed

Gorman, Bryan R; Lu, Junjie; Baccei, Anna; Lowry, Nathan C; Purvis, Jeremy E; Mangoubi, Rami S; Lerou, Paul H

2014-01-01

Human pluripotent stem (hPS) cells are a potential source of cells for medical therapy and an ideal system to study fate decisions in early development. However, hPS cells cultured in vitro exhibit a high degree of heterogeneity, presenting an obstacle to clinical translation. hPS cells grow in spatially patterned colony structures, necessitating quantitative single-cell image analysis. We offer a tool for analyzing the spatial population context of hPS cells that integrates automated fluorescent microscopy with an analysis pipeline. It enables high-throughput detection of colonies at low resolution, with single-cellular and sub-cellular analysis at high resolutions, generating seamless in situ maps of single-cellular data organized by colony. We demonstrate the tool's utility by analyzing inter- and intra-colony heterogeneity of hPS cell cycle regulation and pluripotency marker expression. We measured the heterogeneity within individual colonies by analyzing cell cycle as a function of distance. Cells loosely associated with the outside of the colony are more likely to be in G1, reflecting a less pluripotent state, while cells within the first pluripotent layer are more likely to be in G2, possibly reflecting a G2/M block. Our multi-scale analysis tool groups colony regions into density classes, and cells belonging to those classes have distinct distributions of pluripotency markers and respond differently to DNA damage induction. Lastly, we demonstrate that our pipeline can robustly handle high-content, high-resolution single molecular mRNA FISH data by using novel image processing techniques. Overall, the imaging informatics pipeline presented offers a novel approach to the analysis of hPS cells that includes not only single cell features but also colony wide, and more generally, multi-scale spatial configuration.

An element search ant colony technique for solving virtual machine placement problem

NASA Astrophysics Data System (ADS)

Srija, J.; Rani John, Rose; Kanaga, Grace Mary, Dr.

2017-09-01

The data centres in the cloud environment play a key role in providing infrastructure for ubiquitous computing, pervasive computing, mobile computing etc. This computing technique tries to utilize the available resources in order to provide services. Hence maintaining the resource utilization without wastage of power consumption has become a challenging task for the researchers. In this paper we propose the direct guidance ant colony system for effective mapping of virtual machines to the physical machine with maximal resource utilization and minimal power consumption. The proposed algorithm has been compared with the existing ant colony approach which is involved in solving virtual machine placement problem and thus the proposed algorithm proves to provide better result than the existing technique.

Design of fuzzy classifier for diabetes disease using Modified Artificial Bee Colony algorithm.

PubMed

Beloufa, Fayssal; Chikh, M A

2013-10-01

In this study, diagnosis of diabetes disease, which is one of the most important diseases, is conducted with artificial intelligence techniques. We have proposed a novel Artificial Bee Colony (ABC) algorithm in which a mutation operator is added to an Artificial Bee Colony for improving its performance. When the current best solution cannot be updated, a blended crossover operator (BLX-α) of genetic algorithm is applied, in order to enhance the diversity of ABC, without compromising with the solution quality. This modified version of ABC is used as a new tool to create and optimize automatically the membership functions and rules base directly from data. We take the diabetes dataset used in our work from the UCI machine learning repository. The performances of the proposed method are evaluated through classification rate, sensitivity and specificity values using 10-fold cross-validation method. The obtained classification rate of our method is 84.21% and it is very promising when compared with the previous research in the literature for the same problem. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

A novel metaheuristic for continuous optimization problems: Virus optimization algorithm

NASA Astrophysics Data System (ADS)

Liang, Yun-Chia; Rodolfo Cuevas Juarez, Josue

2016-01-01

A novel metaheuristic for continuous optimization problems, named the virus optimization algorithm (VOA), is introduced and investigated. VOA is an iteratively population-based method that imitates the behaviour of viruses attacking a living cell. The number of viruses grows at each replication and is controlled by an immune system (a so-called 'antivirus') to prevent the explosive growth of the virus population. The viruses are divided into two classes (strong and common) to balance the exploitation and exploration effects. The performance of the VOA is validated through a set of eight benchmark functions, which are also subject to rotation and shifting effects to test its robustness. Extensive comparisons were conducted with over 40 well-known metaheuristic algorithms and their variations, such as artificial bee colony, artificial immune system, differential evolution, evolutionary programming, evolutionary strategy, genetic algorithm, harmony search, invasive weed optimization, memetic algorithm, particle swarm optimization and simulated annealing. The results showed that the VOA is a viable solution for continuous optimization.

Development and applications of various optimization algorithms for diesel engine combustion and emissions optimization

NASA Astrophysics Data System (ADS)

Ogren, Ryan M.

For this work, Hybrid PSO-GA and Artificial Bee Colony Optimization (ABC) algorithms are applied to the optimization of experimental diesel engine performance, to meet Environmental Protection Agency, off-road, diesel engine standards. This work is the first to apply ABC optimization to experimental engine testing. All trials were conducted at partial load on a four-cylinder, turbocharged, John Deere engine using neat-Biodiesel for PSO-GA and regular pump diesel for ABC. Key variables were altered throughout the experiments, including, fuel pressure, intake gas temperature, exhaust gas recirculation flow, fuel injection quantity for two injections, pilot injection timing and main injection timing. Both forms of optimization proved effective for optimizing engine operation. The PSO-GA hybrid was able to find a superior solution to that of ABC within fewer engine runs. Both solutions call for high exhaust gas recirculation to reduce oxide of nitrogen (NOx) emissions while also moving pilot and main fuel injections to near top dead center for improved tradeoffs between NOx and particulate matter.

Research on robust optimization of emergency logistics network considering the time dependence characteristic

NASA Astrophysics Data System (ADS)

WANG, Qingrong; ZHU, Changfeng; LI, Ying; ZHANG, Zhengkun

2017-06-01

Considering the time dependence of emergency logistic network and complexity of the environment that the network exists in, in this paper the time dependent network optimization theory and robust discrete optimization theory are combined, and the emergency logistics dynamic network optimization model with characteristics of robustness is built to maximize the timeliness of emergency logistics. On this basis, considering the complexity of dynamic network and the time dependence of edge weight, an improved ant colony algorithm is proposed to realize the coupling of the optimization algorithm and the network time dependence and robustness. Finally, a case study has been carried out in order to testify validity of this robustness optimization model and its algorithm, and the value of different regulation factors was analyzed considering the importance of the value of the control factor in solving the optimal path. Analysis results show that this model and its algorithm above-mentioned have good timeliness and strong robustness.

Breeding success of a marine central place forager in the context of climate change: A modeling approach.

PubMed

Massardier-Galatà, Lauriane; Morinay, Jennifer; Bailleul, Frédéric; Wajnberg, Eric; Guinet, Christophe; Coquillard, Patrick

2017-01-01

In response to climate warming, a southward shift in productive frontal systems serving as the main foraging sites for many top predator species is likely to occur in Subantarctic areas. Central place foragers, such as seabirds and pinnipeds, are thus likely to cope with an increase in the distance between foraging locations and their land-based breeding colonies. Understanding how central place foragers should modify their foraging behavior in response to changes in prey accessibility appears crucial. A spatially explicit individual-based simulation model (Marine Central Place Forager Simulator (MarCPFS)), including bio-energetic components, was built to evaluate effects of possible changes in prey resources accessibility on individual performances and breeding success. The study was calibrated on a particular example: the Antarctic fur seal (Arctocephalus gazella), which alternates between oceanic areas in which females feed and the land-based colony in which they suckle their young over a 120 days rearing period. Our model shows the importance of the distance covered to feed and prey aggregation which appeared to be key factors to which animals are highly sensitive. Memorization and learning abilities also appear to be essential breeding success traits. Females were found to be most successful for intermediate levels of prey aggregation and short distance to the resource, resulting in optimal female body length. Increased distance to resources due to climate warming should hinder pups' growth and survival while female body length should increase.

Seasonal Food Scarcity Prompts Long-Distance Foraging by a Wild Social Bee.

PubMed

Pope, Nathaniel S; Jha, Shalene

2018-01-01

Foraging is an essential process for mobile animals, and its optimization serves as a foundational theory in ecology and evolution; however, drivers of foraging are rarely investigated across landscapes and seasons. Using a common bumblebee species from the western United States (Bombus vosnesenskii), we ask whether seasonal decreases in food resources prompt changes in foraging behavior and space use. We employ a unique integration of population genetic tools and spatially explicit foraging models to estimate foraging distances and rates of patch visitation for wild bumblebee colonies across three study regions and two seasons. By mapping the locations of 669 wild-caught individual foragers, we find substantial variation in colony-level foraging distances, often exhibiting a 60-fold difference within a study region. Our analysis of visitation rates indicates that foragers display a preference for destination patches with high floral cover and forage significantly farther for these patches, but only in the summer, when landscape-level resources are low. Overall, these results indicate that an increasing proportion of long-distance foraging bouts take place in the summer. Because wild bees are pollinators, their foraging dynamics are of urgent concern, given the potential impacts of global change on their movement and services. The behavioral shift toward long-distance foraging with seasonal declines in food resources suggests a novel, phenologically directed approach to landscape-level pollinator conservation and greater consideration of late-season floral resources in pollinator habitat management.

Breeding success of a marine central place forager in the context of climate change: A modeling approach

PubMed Central

Massardier-Galatà, Lauriane; Morinay, Jennifer; Bailleul, Frédéric; Wajnberg, Eric; Guinet, Christophe; Coquillard, Patrick

2017-01-01

In response to climate warming, a southward shift in productive frontal systems serving as the main foraging sites for many top predator species is likely to occur in Subantarctic areas. Central place foragers, such as seabirds and pinnipeds, are thus likely to cope with an increase in the distance between foraging locations and their land-based breeding colonies. Understanding how central place foragers should modify their foraging behavior in response to changes in prey accessibility appears crucial. A spatially explicit individual-based simulation model (Marine Central Place Forager Simulator (MarCPFS)), including bio-energetic components, was built to evaluate effects of possible changes in prey resources accessibility on individual performances and breeding success. The study was calibrated on a particular example: the Antarctic fur seal (Arctocephalus gazella), which alternates between oceanic areas in which females feed and the land-based colony in which they suckle their young over a 120 days rearing period. Our model shows the importance of the distance covered to feed and prey aggregation which appeared to be key factors to which animals are highly sensitive. Memorization and learning abilities also appear to be essential breeding success traits. Females were found to be most successful for intermediate levels of prey aggregation and short distance to the resource, resulting in optimal female body length. Increased distance to resources due to climate warming should hinder pups’ growth and survival while female body length should increase. PMID:28355282

Effects of colony relocation on diet and productivity of Caspian terns

USGS Publications Warehouse

Roby, D.D.; Collis, K.; Lyons, Donald E.; Craig, D.P.; Adkins, J.Y.; Myers, A.M.; Suryan, R.M.

2002-01-01

We investigated the efficacy of management to reduce the impact of Caspian tern (Sterna caspia) predation on survival of juvenile salmonids (Oncorhynchus spp.) in the Columbia River estuary. Resource managers sought to relocate approximately 9,000 pairs of terns nesting on Rice Island (river km 34) to East Sand Island (river km 8), where terns were expected to prey on fewer juvenile salmonids. Efforts to attract terns to nest on East Sand Island included creation of nesting habitat, use of social attraction techniques, and predator control, with concurrent efforts to discourage terns from nesting on Rice Island. This approach was successful in completely relocating the tern colony from Rice Island to East Sand Island by the third breeding season. Juvenile salmonids decreased and marine forage fishes (i.e., herring, sardine, anchovy, smelt, surfperch, Pacific sand lance) increased in the diet of Caspian terns nesting on East Sand Island, compared with terns nesting on Rice Island. During 1999 and 2000, the diet of terns nesting on Rice Island consisted of 77% and 90% juvenile salmonids, respectively, while during 1999, 2000, and 2001, the diet of terns nesting on East Sand Island consisted of 46%, 47%, and 33% juvenile salmonids, respectively. Nesting success of Caspian terns was consistently and substantially higher on East Sand Island than on Rice Island. These results indicate that relocating the Caspian tern colony was an effective management action for reducing predation on juvenile salmonids without harm to the population of breeding terns, at least in the short term. The success of this management approach largely was a consequence of the nesting and foraging ecology of Caspian terns: the species shifts breeding colony sites frequently in response to changing habitats, and the species is a generalist forager, preying on the most available forage fish near the colony.

The emergence of cooperation from a single cooperative mutant

NASA Astrophysics Data System (ADS)

Cremer, Jonas; Melbinger, Anna; Frey, Erwin

2012-02-01

Population structure is one central condition which promotes the stability of cooperation: If cooperators more likely interact with other cooperators (positive assortment), they keep most of their benefit for themselves and are less exploited by non-cooperators. However, positive assortment can only act successfully if cooperation is already well established in the population such that cooperative individuals can successfully assort. But how can cooperation emerge when starting with a single cooperative mutant? Here we study this issue for a generic situation of microbial systems where microbes regularly form new colonies and show strong population growth. We show how and when the dynamical interplay between colony formation, population growth and evolution within colonies can provoke the emergence of cooperation. In particular, the probability for a single cooperative mutant to succeed is robustly large when colony-formation is fast or comparable to the time-scale of growth within colonies; growth supports cooperation.[4pt] [1] A. Melbinger, J. Cremer, and E. Frey, Evolutionary game theory in growing populations, Phys. Rev. Lett. 105, 178101 (2010)[0pt] [2] J. Cremer, A. Melbinger, and E. Frey, Evolutionary and population dynamics: a coupled approach, arXiv:1108.2604

A new device for monitoring individual activity rhythms of honey bees reveals critical effects of the social environment on behavior.

PubMed

Beer, Katharina; Steffan-Dewenter, Ingolf; Härtel, Stephan; Helfrich-Förster, Charlotte

2016-08-01

Chronobiological studies of individual activity rhythms in social insects can be constrained by the artificial isolation of individuals from their social context. We present a new experimental set-up that simultaneously measures the temperature rhythm in a queen-less but brood raising mini colony and the walking activity rhythms of singly kept honey bees that have indirect social contact with it. Our approach enables monitoring of individual bees in the social context of a mini colony under controlled laboratory conditions. In a pilot experiment, we show that social contact with the mini colony improves the survival of monitored young individuals and affects locomotor activity patterns of young and old bees. When exposed to conflicting Zeitgebers consisting of a light-dark (LD) cycle that is phase-delayed with respect to the mini colony rhythm, rhythms of young and old bees are socially synchronized with the mini colony rhythm, whereas isolated bees synchronize to the LD cycle. We conclude that the social environment is a stronger Zeitgeber than the LD cycle and that our new experimental set-up is well suited for studying the mechanisms of social entrainment in honey bees.

Ecological consequences of colony structure in dynamic ant nest networks.

PubMed

Ellis, Samuel; Franks, Daniel W; Robinson, Elva J H

2017-02-01

Access to resources depends on an individual's position within the environment. This is particularly important to animals that invest heavily in nest construction, such as social insects. Many ant species have a polydomous nesting strategy: a single colony inhabits several spatially separated nests, often exchanging resources between the nests. Different nests in a polydomous colony potentially have differential access to resources, but the ecological consequences of this are unclear. In this study, we investigate how nest survival and budding in polydomous wood ant ( Formica lugubris ) colonies are affected by being part of a multi-nest system. Using field data and novel analytical approaches combining survival models with dynamic network analysis, we show that the survival and budding of nests within a polydomous colony are affected by their position in the nest network structure. Specifically, we find that the flow of resources through a nest, which is based on its position within the wider nest network, determines a nest's likelihood of surviving and of founding new nests. Our results highlight how apparently disparate entities in a biological system can be integrated into a functional ecological unit. We also demonstrate how position within a dynamic network structure can have important ecological consequences.

Parameters-tuning of PID controller for automatic voltage regulators using the African buffalo optimization.

PubMed

Odili, Julius Beneoluchi; Mohmad Kahar, Mohd Nizam; Noraziah, A

2017-01-01

In this paper, an attempt is made to apply the African Buffalo Optimization (ABO) to tune the parameters of a PID controller for an effective Automatic Voltage Regulator (AVR). Existing metaheuristic tuning methods have been proven to be quite successful but there were observable areas that need improvements especially in terms of the system's gain overshoot and steady steady state errors. Using the ABO algorithm where each buffalo location in the herd is a candidate solution to the Proportional-Integral-Derivative parameters was very helpful in addressing these two areas of concern. The encouraging results obtained from the simulation of the PID Controller parameters-tuning using the ABO when compared with the performance of Genetic Algorithm PID (GA-PID), Particle-Swarm Optimization PID (PSO-PID), Ant Colony Optimization PID (ACO-PID), PID, Bacteria-Foraging Optimization PID (BFO-PID) etc makes ABO-PID a good addition to solving PID Controller tuning problems using metaheuristics.

Application of GA, PSO, and ACO algorithms to path planning of autonomous underwater vehicles

NASA Astrophysics Data System (ADS)

Aghababa, Mohammad Pourmahmood; Amrollahi, Mohammad Hossein; Borjkhani, Mehdi

2012-09-01

In this paper, an underwater vehicle was modeled with six dimensional nonlinear equations of motion, controlled by DC motors in all degrees of freedom. Near-optimal trajectories in an energetic environment for underwater vehicles were computed using a numerical solution of a nonlinear optimal control problem (NOCP). An energy performance index as a cost function, which should be minimized, was defined. The resulting problem was a two-point boundary value problem (TPBVP). A genetic algorithm (GA), particle swarm optimization (PSO), and ant colony optimization (ACO) algorithms were applied to solve the resulting TPBVP. Applying an Euler-Lagrange equation to the NOCP, a conjugate gradient penalty method was also adopted to solve the TPBVP. The problem of energetic environments, involving some energy sources, was discussed. Some near-optimal paths were found using a GA, PSO, and ACO algorithms. Finally, the problem of collision avoidance in an energetic environment was also taken into account.

Cuckoo Search Algorithm Based on Repeat-Cycle Asymptotic Self-Learning and Self-Evolving Disturbance for Function Optimization

PubMed Central

Wang, Jie-sheng; Li, Shu-xia; Song, Jiang-di

2015-01-01

In order to improve convergence velocity and optimization accuracy of the cuckoo search (CS) algorithm for solving the function optimization problems, a new improved cuckoo search algorithm based on the repeat-cycle asymptotic self-learning and self-evolving disturbance (RC-SSCS) is proposed. A disturbance operation is added into the algorithm by constructing a disturbance factor to make a more careful and thorough search near the bird's nests location. In order to select a reasonable repeat-cycled disturbance number, a further study on the choice of disturbance times is made. Finally, six typical test functions are adopted to carry out simulation experiments, meanwhile, compare algorithms of this paper with two typical swarm intelligence algorithms particle swarm optimization (PSO) algorithm and artificial bee colony (ABC) algorithm. The results show that the improved cuckoo search algorithm has better convergence velocity and optimization accuracy. PMID:26366164

Real-time bacterial microcolony counting using on-chip microscopy

NASA Astrophysics Data System (ADS)

Jung, Jae Hee; Lee, Jung Eun

2016-02-01

Observing microbial colonies is the standard method for determining the microbe titer and investigating the behaviors of microbes. Here, we report an automated, real-time bacterial microcolony-counting system implemented on a wide field-of-view (FOV), on-chip microscopy platform, termed ePetri. Using sub-pixel sweeping microscopy (SPSM) with a super-resolution algorithm, this system offers the ability to dynamically track individual bacterial microcolonies over a wide FOV of 5.7 mm × 4.3 mm without requiring a moving stage or lens. As a demonstration, we obtained high-resolution time-series images of S. epidermidis at 20-min intervals. We implemented an image-processing algorithm to analyze the spatiotemporal distribution of microcolonies, the development of which could be observed from a single bacterial cell. Test bacterial colonies with a minimum diameter of 20 μm could be enumerated within 6 h. We showed that our approach not only provides results that are comparable to conventional colony-counting assays but also can be used to monitor the dynamics of colony formation and growth. This microcolony-counting system using on-chip microscopy represents a new platform that substantially reduces the detection time for bacterial colony counting. It uses chip-scale image acquisition and is a simple and compact solution for the automation of colony-counting assays and microbe behavior analysis with applications in antibacterial drug discovery.

Non-Gaussian noise-weakened stability in a foraging colony system with time delay

NASA Astrophysics Data System (ADS)

Dong, Xiaohui; Zeng, Chunhua; Yang, Fengzao; Guan, Lin; Xie, Qingshuang; Duan, Weilong

2018-02-01

In this paper, the dynamical properties in a foraging colony system with time delay and non-Gaussian noise were investigated. Using delay Fokker-Planck approach, the stationary probability distribution (SPD), the associated relaxation time (ART) and normalization correlation function (NCF) are obtained, respectively. The results show that: (i) the time delay and non-Gaussian noise can induce transition from a single peak to double peaks in the SPD, i.e., a type of bistability occurring in a foraging colony system where time delay and non-Gaussian noise not only cause transitions between stable states, but also construct the states themselves. Numerical simulations are presented and are in good agreement with the approximate theoretical results; (ii) there exists a maximum in the ART as a function of the noise intensity, this maximum for ART is identified as the characteristic of the non-Gaussian noise-weakened stability of the foraging colonies in the steady state; (iii) the ART as a function of the noise correlation time exhibits a maximum and a minimum, where the minimum for ART is identified as the signature of the non-Gaussian noise-enhanced stability of the foraging colonies; and (iv) the time delay can enhance the stability of the foraging colonies in the steady state, while the departure from Gaussian noise can weaken it, namely, the time delay and departure from Gaussian noise play opposite roles in ART or NCF.

Real-time bacterial microcolony counting using on-chip microscopy

PubMed Central

Jung, Jae Hee; Lee, Jung Eun

2016-01-01

Observing microbial colonies is the standard method for determining the microbe titer and investigating the behaviors of microbes. Here, we report an automated, real-time bacterial microcolony-counting system implemented on a wide field-of-view (FOV), on-chip microscopy platform, termed ePetri. Using sub-pixel sweeping microscopy (SPSM) with a super-resolution algorithm, this system offers the ability to dynamically track individual bacterial microcolonies over a wide FOV of 5.7 mm × 4.3 mm without requiring a moving stage or lens. As a demonstration, we obtained high-resolution time-series images of S. epidermidis at 20-min intervals. We implemented an image-processing algorithm to analyze the spatiotemporal distribution of microcolonies, the development of which could be observed from a single bacterial cell. Test bacterial colonies with a minimum diameter of 20 μm could be enumerated within 6 h. We showed that our approach not only provides results that are comparable to conventional colony-counting assays but also can be used to monitor the dynamics of colony formation and growth. This microcolony-counting system using on-chip microscopy represents a new platform that substantially reduces the detection time for bacterial colony counting. It uses chip-scale image acquisition and is a simple and compact solution for the automation of colony-counting assays and microbe behavior analysis with applications in antibacterial drug discovery. PMID:26902822

Warring arthropod societies: Social spider colonies can delay annihilation by predatory ants via reduced apparency and increased group size.

PubMed

Keiser, Carl N; Wright, Colin M; Pruitt, Jonathan N

2015-10-01

Sociality provides individuals with benefits via collective foraging and anti-predator defense. One of the costs of living in large groups, however, is increased apparency to natural enemies. Here, we test how the individual-level and collective traits of spider societies can increase the risk of discovery and death by predatory ants. We transplanted colonies of the social spider Stegodyphus dumicola into a habitat dense with one of their top predators, the pugnacious ant Anoplolepis custodiens. With three different experiments, we test how colony-wide survivorship in a predator-dense habitat can be altered by colony apparency (i.e., the presence of a capture web), group size, and group composition (i.e., the proportion of bold and shy personality types present). We also test how spiders' social context (i.e., living solitarily vs. among conspecifics) modifies their behaviour toward ants in their capture web. Colonies with capture webs intact were discovered by predatory ants on average 25% faster than colonies with the capture web removed, and all discovered colonies eventually collapsed and succumbed to predation. However, the lag time from discovery by ants to colony collapse was greater for colonies containing more individuals. The composition of individual personality types in the group had no influence on survivorship. Spiders in a social group were more likely to approach ants caught in their web than were isolated spiders. Isolated spiders were more likely to attack a safe prey item (a moth) than they were to attack ants and were more likely to retreat from ants after contact than they were after contact with moths. Together, our data suggest that the physical structures produced by large animal societies can increase their apparency to natural enemies, though larger groups can facilitate a longer lag time between discovery and demise. Lastly, the interaction between spiders and predatory ants seems to depend on the social context in which spiders reside. Copyright © 2015 Elsevier B.V. All rights reserved.

Video bioinformatics analysis of human embryonic stem cell colony growth.

PubMed

Lin, Sabrina; Fonteno, Shawn; Satish, Shruthi; Bhanu, Bir; Talbot, Prue

2010-05-20

Because video data are complex and are comprised of many images, mining information from video material is difficult to do without the aid of computer software. Video bioinformatics is a powerful quantitative approach for extracting spatio-temporal data from video images using computer software to perform dating mining and analysis. In this article, we introduce a video bioinformatics method for quantifying the growth of human embryonic stem cells (hESC) by analyzing time-lapse videos collected in a Nikon BioStation CT incubator equipped with a camera for video imaging. In our experiments, hESC colonies that were attached to Matrigel were filmed for 48 hours in the BioStation CT. To determine the rate of growth of these colonies, recipes were developed using CL-Quant software which enables users to extract various types of data from video images. To accurately evaluate colony growth, three recipes were created. The first segmented the image into the colony and background, the second enhanced the image to define colonies throughout the video sequence accurately, and the third measured the number of pixels in the colony over time. The three recipes were run in sequence on video data collected in a BioStation CT to analyze the rate of growth of individual hESC colonies over 48 hours. To verify the truthfulness of the CL-Quant recipes, the same data were analyzed manually using Adobe Photoshop software. When the data obtained using the CL-Quant recipes and Photoshop were compared, results were virtually identical, indicating the CL-Quant recipes were truthful. The method described here could be applied to any video data to measure growth rates of hESC or other cells that grow in colonies. In addition, other video bioinformatics recipes can be developed in the future for other cell processes such as migration, apoptosis, and cell adhesion.

A New Method of Space Travel Optimized for Space Tourism and Colonization

NASA Astrophysics Data System (ADS)

Turek, Philip A.

2006-01-01

High costs associated with expendable rockets are stifling the development of permanent space colonies. A new method of space travel is presented that enjoys significantly increased performance and reduced cost relative to competing concepts. Based on recycling the kinetic energy of an arriving spacecraft, up to 200 MW of average electrical power is generated and sustained for 2 minutes, and is immediately applied in launching a departing partner spacecraft. The resulting required delta vee for a round trip between low Earth orbit (LEO) and geosynchronous orbit (GEO) drops from 7.6 km/s to 0.54 km/s when 3 recycling stations with an 80 % energy coupling efficiency are used to exchange kinetic energy between 8 partner spacecraft transiting the same route. This method is well suited for round trip high volume space travel such as space tourism traffic to LEO, lunar orbit, and beyond. As the kinetic energy of an arriving spacecraft is the power source for launching departing spacecraft, nascent lunar colonies can electrically launch 26,000 kg payloads long before sustained 100 MW level power supplies become locally available. A pair of recycling stations at an orbiting space colony construction site provides a resource of net impulse, net torque, and electrical power to the colony irrespective of the contents of the arriving payloads. Kinetic energy recycling technology, configuration, operations, and near Earth applications are described.

Detection of Endolithes Using Infrared Spectroscopy

NASA Astrophysics Data System (ADS)

Dumas, S.; Dutil, Y.; Joncas, G.

2009-12-01

On Earth, the Dry Valleys of Antarctica provide the closest martian-like environment for the study of extremophiles. Colonies of bacterias are protected from the freezing temperatures, the drought and UV light. They represent almost half of the biomass of those regions. Due to their resilience, endolithes are one possible model of martian biota. We propose to use infrared spectroscopy to remotely detect those colonies even if there is no obvious sign of their presence. This remote sensing approach reduces the risk of contamination or damage to the samples.

Candida parapsilosis biofilm identification by Raman spectroscopy.

PubMed

Samek, Ota; Mlynariková, Katarina; Bernatová, Silvie; Ježek, Jan; Krzyžánek, Vladislav; Šiler, Martin; Zemánek, Pavel; Růžička, Filip; Holá, Veronika; Mahelová, Martina

2014-12-22

Colonies of Candida parapsilosis on culture plates were probed directly in situ using Raman spectroscopy for rapid identification of specific strains separated by a given time intervals (up to months apart). To classify the Raman spectra, data analysis was performed using the approach of principal component analysis (PCA). The analysis of the data sets generated during the scans of individual colonies reveals that despite the inhomogeneity of the biological samples unambiguous associations to individual strains (two biofilm-positive and two biofilm-negative) could be made.

Candida parapsilosis Biofilm Identification by Raman Spectroscopy

PubMed Central

Samek, Ota; Mlynariková, Katarina; Bernatová, Silvie; Ježek, Jan; Krzyžánek, Vladislav; Šiler, Martin; Zemánek, Pavel; Růžička, Filip; Holá, Veronika; Mahelová, Martina

2014-01-01

Colonies of Candida parapsilosis on culture plates were probed directly in situ using Raman spectroscopy for rapid identification of specific strains separated by a given time intervals (up to months apart). To classify the Raman spectra, data analysis was performed using the approach of principal component analysis (PCA). The analysis of the data sets generated during the scans of individual colonies reveals that despite the inhomogeneity of the biological samples unambiguous associations to individual strains (two biofilm-positive and two biofilm-negative) could be made. PMID:25535081

Examining the conflation of multiculturalism, sexism, and religious fundamentalism through Taylor and Bakhtin: expanding post-colonial feminist epistemology.

PubMed

Racine, Louise

2009-01-01

In this post-9/11 era marked by religious and ethnic conflicts and the rise of cultural intolerance, ambiguities arising from the conflation of multiculturalism, sexism, and religious fundamentalism jeopardize the delivery of culturally safe nursing care to non-Western populations. This new social reality requires nurses to develop a heightened awareness of health issues pertaining to racism and ethnocentrism to provide culturally safe care to non-Western immigrants or refugees. Through the lens of post-colonial feminism, this paper explores the challenge of providing culturally safe nursing care in the context of the post-9/11 in Canadian healthcare settings. A critical appraisal of the literature demonstrates that post-colonial feminism, despite some limitations, remains a valuable theoretical perspective to apply in cultural nursing research and develop culturally safe nursing practice. Post-colonial feminism offers the analytical lens to understand how health, social and cultural context, race and gender intersect to impact on non-Western populations' health. However, an uncritical application of post-colonial feminism may not serve racialized men's and women's interests because of its essentialist risk. Post-colonial feminism must expand its epistemological assumptions to integrate Taylor's concept of identity and recognition and Bakhtin's concepts of dialogism and unfinalizability to explore non-Western populations' health issues and the context of nursing practice. This would strengthen the theoretical adequacy of post-colonial feminist approaches in unveiling the process of racialization that arises from the conflation of multiculturalism, sexism, and religious fundamentalism in Western healthcare settings.

Symbiotic flagellate protists as cryptic drivers of adaptation and invasiveness of the subterranean termite Reticulitermes grassei Clément.

PubMed

Duarte, Sónia; Nobre, Tânia; Borges, Paulo A V; Nunes, Lina

2018-06-01

Changes in flagellate protist communities of subterranean termite Reticulitermes grassei across different locations were evaluated following four predictions: (i) Rural endemic (Portugal mainland) termite populations will exhibit high diversity of symbionts; (ii) invasive urban populations (Horta city, Faial island, Azores), on the contrary, will exhibit lower diversity of symbionts, showing high similarity of symbiont assemblages through environmental filtering; (iii) recent historical colonization of isolated regions-as the case of islands-will imply a loss of symbiont diversity; and (iv) island isolation will trigger a change in colony breeding structure toward a less aggressive behavior. Symbiont flagellate protist communities were morphologically identified, and species richness and relative abundances, as well as biodiversity indices, were used to compare symbiotic communities in colonies from urban and rural environments and between island invasive and mainland endemic populations. To evaluate prediction on the impact of isolation (iv), aggression tests were performed among termites comprising island invasive and mainland endemic populations. A core group of flagellates and secondary facultative symbionts was identified. Termites from rural environments showed, in the majority of observed colonies, more diverse and abundant protist communities, probably confirming prediction (i). Corroborating prediction (ii), the two least diverse communities belong to termites captured inside urban areas. The Azorean invasive termite colonies had more diverse protist communities than expected and prediction (iii) which was not verified within this study. Termites from mainland populations showed a high level of aggressiveness between neighboring colonies, in contrast to the invasive colonies from Horta city, which were not aggressive to neighbors according to prediction (iv). The symbiotic flagellate community of R. grassei showed the ability to change in a way that might be consistent with adaptation to available conditions, possibly contributing to optimization of the colonization of new habitats and spreading of its distribution area, highlighting R. grassei potential as an invasive species.

A model for the optimization of the detection and eradication of isolated gypsy moth colonies

Treesearch

Tiffany L. Bogich; Andrew Liebhold; Katriona Shea

2007-01-01

Biological invasions of pest species pose a threat to the stability of ecosystems, both natural and managed (Liebhold et al. 1995, Shogren and Tschirhart 2005). Considerable effort is expended by national and local governments on excluding alien species via detection and eradication of invading populations, but these efforts are not necessarily designed in the most...

At-Least Version of the Generalized Minimum Spanning Tree Problem: Optimization Through Ant Colony System and Genetic Algorithms

NASA Technical Reports Server (NTRS)

Janich, Karl W.

2005-01-01

The At-Least version of the Generalized Minimum Spanning Tree Problem (L-GMST) is a problem in which the optimal solution connects all defined clusters of nodes in a given network at a minimum cost. The L-GMST is NPHard; therefore, metaheuristic algorithms have been used to find reasonable solutions to the problem as opposed to computationally feasible exact algorithms, which many believe do not exist for such a problem. One such metaheuristic uses a swarm-intelligent Ant Colony System (ACS) algorithm, in which agents converge on a solution through the weighing of local heuristics, such as the shortest available path and the number of agents that recently used a given path. However, in a network using a solution derived from the ACS algorithm, some nodes may move around to different clusters and cause small changes in the network makeup. Rerunning the algorithm from the start would be somewhat inefficient due to the significance of the changes, so a genetic algorithm based on the top few solutions found in the ACS algorithm is proposed to quickly and efficiently adapt the network to these small changes.

An Ant Colony Optimization Based Feature Selection for Web Page Classification

PubMed Central

2014-01-01

The increased popularity of the web has caused the inclusion of huge amount of information to the web, and as a result of this explosive information growth, automated web page classification systems are needed to improve search engines' performance. Web pages have a large number of features such as HTML/XML tags, URLs, hyperlinks, and text contents that should be considered during an automated classification process. The aim of this study is to reduce the number of features to be used to improve runtime and accuracy of the classification of web pages. In this study, we used an ant colony optimization (ACO) algorithm to select the best features, and then we applied the well-known C4.5, naive Bayes, and k nearest neighbor classifiers to assign class labels to web pages. We used the WebKB and Conference datasets in our experiments, and we showed that using the ACO for feature selection improves both accuracy and runtime performance of classification. We also showed that the proposed ACO based algorithm can select better features with respect to the well-known information gain and chi square feature selection methods. PMID:25136678

Intelligent Method for Diagnosing Structural Faults of Rotating Machinery Using Ant Colony Optimization

PubMed Central

Li, Ke; Chen, Peng

2011-01-01

Structural faults, such as unbalance, misalignment and looseness, etc., often occur in the shafts of rotating machinery. These faults may cause serious machine accidents and lead to great production losses. This paper proposes an intelligent method for diagnosing structural faults of rotating machinery using ant colony optimization (ACO) and relative ratio symptom parameters (RRSPs) in order to detect faults and distinguish fault types at an early stage. New symptom parameters called “relative ratio symptom parameters” are defined for reflecting the features of vibration signals measured in each state. Synthetic detection index (SDI) using statistical theory has also been defined to evaluate the applicability of the RRSPs. The SDI can be used to indicate the fitness of a RRSP for ACO. Lastly, this paper also compares the proposed method with the conventional neural networks (NN) method. Practical examples of fault diagnosis for a centrifugal fan are provided to verify the effectiveness of the proposed method. The verification results show that the structural faults often occurring in the centrifugal fan, such as unbalance, misalignment and looseness states are effectively identified by the proposed method, while these faults are difficult to detect using conventional neural networks. PMID:22163833

Intelligent method for diagnosing structural faults of rotating machinery using ant colony optimization.

PubMed

Li, Ke; Chen, Peng

2011-01-01

Structural faults, such as unbalance, misalignment and looseness, etc., often occur in the shafts of rotating machinery. These faults may cause serious machine accidents and lead to great production losses. This paper proposes an intelligent method for diagnosing structural faults of rotating machinery using ant colony optimization (ACO) and relative ratio symptom parameters (RRSPs) in order to detect faults and distinguish fault types at an early stage. New symptom parameters called "relative ratio symptom parameters" are defined for reflecting the features of vibration signals measured in each state. Synthetic detection index (SDI) using statistical theory has also been defined to evaluate the applicability of the RRSPs. The SDI can be used to indicate the fitness of a RRSP for ACO. Lastly, this paper also compares the proposed method with the conventional neural networks (NN) method. Practical examples of fault diagnosis for a centrifugal fan are provided to verify the effectiveness of the proposed method. The verification results show that the structural faults often occurring in the centrifugal fan, such as unbalance, misalignment and looseness states are effectively identified by the proposed method, while these faults are difficult to detect using conventional neural networks.

Ant Colony Optimization Algorithm for Centralized Dynamic Channel Allocation in Multi-Cell OFDMA Systems

NASA Astrophysics Data System (ADS)

Kim, Hyo-Su; Kim, Dong-Hoi

The dynamic channel allocation (DCA) scheme in multi-cell systems causes serious inter-cell interference (ICI) problem to some existing calls when channels for new calls are allocated. Such a problem can be addressed by advanced centralized DCA design that is able to minimize ICI. Thus, in this paper, a centralized DCA is developed for the downlink of multi-cell orthogonal frequency division multiple access (OFDMA) systems with full spectral reuse. However, in practice, as the search space of channel assignment for centralized DCA scheme in multi-cell systems grows exponentially with the increase of the number of required calls, channels, and cells, it becomes an NP-hard problem and is currently too complicated to find an optimum channel allocation. In this paper, we propose an ant colony optimization (ACO) based DCA scheme using a low-complexity ACO algorithm which is a kind of heuristic algorithm in order to solve the aforementioned problem. Simulation results demonstrate significant performance improvements compared to the existing schemes in terms of the grade of service (GoS) performance and the forced termination probability of existing calls without degrading the system performance of the average throughput.

Response threshold variance as a basis of collective rationality

PubMed Central

Yamamoto, Tatsuhiro

2017-01-01

Determining the optimal choice among multiple options is necessary in various situations, and the collective rationality of groups has recently become a major topic of interest. Social insects are thought to make such optimal choices by collecting individuals' responses relating to an option's value (=a quality-graded response). However, this behaviour cannot explain the collective rationality of brains because neurons can make only ‘yes/no’ responses on the basis of the response threshold. Here, we elucidate the basic mechanism underlying the collective rationality of such simple units and show that an ant species uses this mechanism. A larger number of units respond ‘yes’ to the best option available to a collective decision-maker using only the yes/no mechanism; thus, the best option is always selected by majority decision. Colonies of the ant Myrmica kotokui preferred the better option in a binary choice experiment. The preference of a colony was demonstrated by the workers, which exhibited variable thresholds between two options' qualities. Our results demonstrate how a collective decision-maker comprising simple yes/no judgement units achieves collective rationality without using quality-graded responses. This mechanism has broad applicability to collective decision-making in brain neurons, swarm robotics and human societies. PMID:28484636

Response threshold variance as a basis of collective rationality.

PubMed

Yamamoto, Tatsuhiro; Hasegawa, Eisuke

2017-04-01

Determining the optimal choice among multiple options is necessary in various situations, and the collective rationality of groups has recently become a major topic of interest. Social insects are thought to make such optimal choices by collecting individuals' responses relating to an option's value (=a quality-graded response). However, this behaviour cannot explain the collective rationality of brains because neurons can make only 'yes/no' responses on the basis of the response threshold. Here, we elucidate the basic mechanism underlying the collective rationality of such simple units and show that an ant species uses this mechanism. A larger number of units respond 'yes' to the best option available to a collective decision-maker using only the yes/no mechanism; thus, the best option is always selected by majority decision. Colonies of the ant Myrmica kotokui preferred the better option in a binary choice experiment. The preference of a colony was demonstrated by the workers, which exhibited variable thresholds between two options' qualities. Our results demonstrate how a collective decision-maker comprising simple yes/no judgement units achieves collective rationality without using quality-graded responses. This mechanism has broad applicability to collective decision-making in brain neurons, swarm robotics and human societies.

An in vivo library-versus-library selection of optimized protein-protein interactions.

PubMed

Pelletier, J N; Arndt, K M; Plückthun, A; Michnick, S W

1999-07-01

We describe a rapid and efficient in vivo library-versus-library screening strategy for identifying optimally interacting pairs of heterodimerizing polypeptides. Two leucine zipper libraries, semi-randomized at the positions adjacent to the hydrophobic core, were genetically fused to either one of two designed fragments of the enzyme murine dihydrofolate reductase (mDHFR), and cotransformed into Escherichia coli. Interaction between the library polypeptides reconstituted enzymatic activity of mDHFR, allowing bacterial growth. Analysis of the resulting colonies revealed important biases in the zipper sequences relative to the original libraries, which are consistent with selection for stable, heterodimerizing pairs. Using more weakly associating mDHFR fragments, we increased the stringency of selection. We enriched the best-performing leucine zipper pairs by multiple passaging of the pooled, selected colonies in liquid culture, as the best pairs allowed for better bacterial propagation. This competitive growth allowed small differences among the pairs to be amplified, and different sequence positions were enriched at different rates. We applied these selection processes to a library-versus-library sample of 2.0 x 10(6) combinations and selected a novel leucine zipper pair that may be appropriate for use in further in vivo heterodimerization strategies.

Proposed algorithm to improve job shop production scheduling using ant colony optimization method

NASA Astrophysics Data System (ADS)

Pakpahan, Eka KA; Kristina, Sonna; Setiawan, Ari

2017-12-01

This paper deals with the determination of job shop production schedule on an automatic environment. On this particular environment, machines and material handling system are integrated and controlled by a computer center where schedule were created and then used to dictate the movement of parts and the operations at each machine. This setting is usually designed to have an unmanned production process for a specified interval time. We consider here parts with various operations requirement. Each operation requires specific cutting tools. These parts are to be scheduled on machines each having identical capability, meaning that each machine is equipped with a similar set of cutting tools therefore is capable of processing any operation. The availability of a particular machine to process a particular operation is determined by the remaining life time of its cutting tools. We proposed an algorithm based on the ant colony optimization method and embedded them on matlab software to generate production schedule which minimize the total processing time of the parts (makespan). We test the algorithm on data provided by real industry and the process shows a very short computation time. This contributes a lot to the flexibility and timelines targeted on an automatic environment.

When the lowest energy does not induce native structures: parallel minimization of multi-energy values by hybridizing searching intelligences.

PubMed

Lü, Qiang; Xia, Xiao-Yan; Chen, Rong; Miao, Da-Jun; Chen, Sha-Sha; Quan, Li-Jun; Li, Hai-Ou

2012-01-01

Protein structure prediction (PSP), which is usually modeled as a computational optimization problem, remains one of the biggest challenges in computational biology. PSP encounters two difficult obstacles: the inaccurate energy function problem and the searching problem. Even if the lowest energy has been luckily found by the searching procedure, the correct protein structures are not guaranteed to obtain. A general parallel metaheuristic approach is presented to tackle the above two problems. Multi-energy functions are employed to simultaneously guide the parallel searching threads. Searching trajectories are in fact controlled by the parameters of heuristic algorithms. The parallel approach allows the parameters to be perturbed during the searching threads are running in parallel, while each thread is searching the lowest energy value determined by an individual energy function. By hybridizing the intelligences of parallel ant colonies and Monte Carlo Metropolis search, this paper demonstrates an implementation of our parallel approach for PSP. 16 classical instances were tested to show that the parallel approach is competitive for solving PSP problem. This parallel approach combines various sources of both searching intelligences and energy functions, and thus predicts protein conformations with good quality jointly determined by all the parallel searching threads and energy functions. It provides a framework to combine different searching intelligence embedded in heuristic algorithms. It also constructs a container to hybridize different not-so-accurate objective functions which are usually derived from the domain expertise.

When the Lowest Energy Does Not Induce Native Structures: Parallel Minimization of Multi-Energy Values by Hybridizing Searching Intelligences

PubMed Central

Lü, Qiang; Xia, Xiao-Yan; Chen, Rong; Miao, Da-Jun; Chen, Sha-Sha; Quan, Li-Jun; Li, Hai-Ou

2012-01-01

Background Protein structure prediction (PSP), which is usually modeled as a computational optimization problem, remains one of the biggest challenges in computational biology. PSP encounters two difficult obstacles: the inaccurate energy function problem and the searching problem. Even if the lowest energy has been luckily found by the searching procedure, the correct protein structures are not guaranteed to obtain. Results A general parallel metaheuristic approach is presented to tackle the above two problems. Multi-energy functions are employed to simultaneously guide the parallel searching threads. Searching trajectories are in fact controlled by the parameters of heuristic algorithms. The parallel approach allows the parameters to be perturbed during the searching threads are running in parallel, while each thread is searching the lowest energy value determined by an individual energy function. By hybridizing the intelligences of parallel ant colonies and Monte Carlo Metropolis search, this paper demonstrates an implementation of our parallel approach for PSP. 16 classical instances were tested to show that the parallel approach is competitive for solving PSP problem. Conclusions This parallel approach combines various sources of both searching intelligences and energy functions, and thus predicts protein conformations with good quality jointly determined by all the parallel searching threads and energy functions. It provides a framework to combine different searching intelligence embedded in heuristic algorithms. It also constructs a container to hybridize different not-so-accurate objective functions which are usually derived from the domain expertise. PMID:23028708

Modified Discrete Grey Wolf Optimizer Algorithm for Multilevel Image Thresholding

PubMed Central

Sun, Lijuan; Guo, Jian; Xu, Bin; Li, Shujing

2017-01-01

The computation of image segmentation has become more complicated with the increasing number of thresholds, and the option and application of the thresholds in image thresholding fields have become an NP problem at the same time. The paper puts forward the modified discrete grey wolf optimizer algorithm (MDGWO), which improves on the optimal solution updating mechanism of the search agent by the weights. Taking Kapur's entropy as the optimized function and based on the discreteness of threshold in image segmentation, the paper firstly discretizes the grey wolf optimizer (GWO) and then proposes a new attack strategy by using the weight coefficient to replace the search formula for optimal solution used in the original algorithm. The experimental results show that MDGWO can search out the optimal thresholds efficiently and precisely, which are very close to the result examined by exhaustive searches. In comparison with the electromagnetism optimization (EMO), the differential evolution (DE), the Artifical Bee Colony (ABC), and the classical GWO, it is concluded that MDGWO has advantages over the latter four in terms of image segmentation quality and objective function values and their stability. PMID:28127305

Comparison of Inoculation with the InoqulA and WASP Automated Systems with Manual Inoculation

PubMed Central

Croxatto, Antony; Dijkstra, Klaas; Prod'hom, Guy

2015-01-01

The quality of sample inoculation is critical for achieving an optimal yield of discrete colonies in both monomicrobial and polymicrobial samples to perform identification and antibiotic susceptibility testing. Consequently, we compared the performance between the InoqulA (BD Kiestra), the WASP (Copan), and manual inoculation methods. Defined mono- and polymicrobial samples of 4 bacterial species and cloudy urine specimens were inoculated on chromogenic agar by the InoqulA, the WASP, and manual methods. Images taken with ImagA (BD Kiestra) were analyzed with the VisionLab version 3.43 image analysis software to assess the quality of growth and to prevent subjective interpretation of the data. A 3- to 10-fold higher yield of discrete colonies was observed following automated inoculation with both the InoqulA and WASP systems than that with manual inoculation. The difference in performance between automated and manual inoculation was mainly observed at concentrations of >106 bacteria/ml. Inoculation with the InoqulA system allowed us to obtain significantly more discrete colonies than the WASP system at concentrations of >107 bacteria/ml. However, the level of difference observed was bacterial species dependent. Discrete colonies of bacteria present in 100- to 1,000-fold lower concentrations than the most concentrated populations in defined polymicrobial samples were not reproducibly recovered, even with the automated systems. The analysis of cloudy urine specimens showed that InoqulA inoculation provided a statistically significantly higher number of discrete colonies than that with WASP and manual inoculation. Consequently, the automated InoqulA inoculation greatly decreased the requirement for bacterial subculture and thus resulted in a significant reduction in the time to results, laboratory workload, and laboratory costs. PMID:25972424

Ant colony optimisation inversion of surface and borehole magnetic data under lithological constraints

NASA Astrophysics Data System (ADS)

Liu, Shuang; Hu, Xiangyun; Liu, Tianyou; Xi, Yufei; Cai, Jianchao; Zhang, Henglei

2015-01-01

The ant colony optimisation algorithm has successfully been used to invert for surface magnetic data. However, the resolution of the distributions of the recovered physical property for deeply buried magnetic sources is not generally very high because of geophysical ambiguities. We use three approaches to deal with this problem. First, the observed surface magnetic data are taken together with the three-component borehole magnetic anomalies to recover the distributions of the physical properties. This cooperative inversion strategy improves the resolution of the inversion results in the vertical direction. Additionally, as the ant colony tours the discrete nodes, we force it to visit the nodes with physical properties that agree with the drilled lithologies. These lithological constraints reduce the non-uniqueness of the inversion problem. Finally, we also implement a K-means cluster analysis for the distributions of the magnetic cells after each iteration, in order to separate the distributions of magnetisation intensity instead of concentrating the distribution in a single area. We tested our method using synthetic data and found that all tests returned favourable results. In the case study of the Mengku iron-ore deposit in northwest China, the recovered distributions of magnetisation are in good agreement with the locations and shapes of the magnetite orebodies as inferred by drillholes. Uncertainty analysis shows that the ant colony algorithm is robust in the presence of noise and that the proposed approaches significantly improve the quality of the inversion results.

MGA trajectory planning with an ACO-inspired algorithm

NASA Astrophysics Data System (ADS)

Ceriotti, Matteo; Vasile, Massimiliano

2010-11-01

Given a set of celestial bodies, the problem of finding an optimal sequence of swing-bys, deep space manoeuvres (DSM) and transfer arcs connecting the elements of the set is combinatorial in nature. The number of possible paths grows exponentially with the number of celestial bodies. Therefore, the design of an optimal multiple gravity assist (MGA) trajectory is a NP-hard mixed combinatorial-continuous problem. Its automated solution would greatly improve the design of future space missions, allowing the assessment of a large number of alternative mission options in a short time. This work proposes to formulate the complete automated design of a multiple gravity assist trajectory as an autonomous planning and scheduling problem. The resulting scheduled plan will provide the optimal planetary sequence and a good estimation of the set of associated optimal trajectories. The trajectory model consists of a sequence of celestial bodies connected by two-dimensional transfer arcs containing one DSM. For each transfer arc, the position of the planet and the spacecraft, at the time of arrival, are matched by varying the pericentre of the preceding swing-by, or the magnitude of the launch excess velocity, for the first arc. For each departure date, this model generates a full tree of possible transfers from the departure to the destination planet. Each leaf of the tree represents a planetary encounter and a possible way to reach that planet. An algorithm inspired by ant colony optimization (ACO) is devised to explore the space of possible plans. The ants explore the tree from departure to destination adding one node at the time: every time an ant is at a node, a probability function is used to select a feasible direction. This approach to automatic trajectory planning is applied to the design of optimal transfers to Saturn and among the Galilean moons of Jupiter. Solutions are compared to those found through more traditional genetic-algorithm techniques.

Multiscale habitat selection by burrowing owls in black-tailed prairie dog colonies

USGS Publications Warehouse

Lantz, S.J.; Conway, C.J.; Anderson, S.H.

2007-01-01

Some populations of western burrowing owls (Athene cunicularia hypugaea) have declined in recent decades. To design and implement effective recovery efforts, we need a better understanding of how distribution and demographic traits are influenced by habitat quality. To this end, we measured spatial patterns of burrowing owl breeding habitat selection within black-tailed prairie dog (Cynomys ludovicianus) colonies in northeastern Wyoming, USA. We compared burrow-, site-, colony-, and landscape-scale habitat parameters between burrowing owl nest burrows (n = 105) and unoccupied burrows (n = 85). We sampled 4 types of prairie dog colonies: 1) owl-occupied, active with prairie dogs (n = 16); 2) owl-occupied, inactive (n = 13); 3) owl-unoccupied, active (n = 14); and 4) owl-unoccupied, inactive (n = 14). We used an information-theoretic approach to examine a set of candidate models of burrowing owl nest-site selection. The model with the most support included variables at all 4 spatial scales, and results were consistent among the 4 types of prairie dog colonies. Nest burrows had longer tunnels, more available burrows within 30 m, and less shrub cover within 30 m, more prairie dog activity within 100 m, and were closer to water than unoccupied burrows. The model correctly classified 76% of cases, all model coefficients were stable, and the model had high predictive ability. Based on our results, we recommend actions to ensure persistence of the remaining prairie dog colonies as an important management strategy for burrowing owl conservation in the Great Plains of North America.

Determinants of between-year burrow re-occupation in a colony of the European bee-eater Merops apiaster.

PubMed

Brust, Vera; Bastian, Hans-Valentin; Bastian, Anita; Schmoll, Tim

2015-08-01

Re-occupation of existing nesting burrows in the European bee-eater Merops apiaster has only rarely - and if so mostly anecdotically - been documented in the literature record, although such behavior would substantially save time and energy. In this study, we quantify burrow re-occupation in a German colony over a period of eleven years and identify ecological variables determining reuse probability. Of 179 recorded broods, 54% took place in a reused burrow and the overall probability that one of 75 individually recognized burrows would be reused in a given subsequent year was estimated as 26.4%. This indicates that between-year burrow reuse is a common behavior in the study colony which contrasts with findings from studies in other colonies. Furthermore, burrow re-occupation probability declined highly significantly with increasing age of the breeding wall. Statistical separation of within- and between-burrow effects of the age of the breeding wall revealed that a decline in re-occupation probability with individual burrow age was responsible for this and not a selective disappearance of burrows with high re-occupation probability over time. Limited duty cycles of individual burrows may be caused by accumulating detritus or decreasing stability with increasing burrow age. Alternatively, burrow fidelity may presuppose pair fidelity which may also explain the observed restricted burrow reuse duty cycles. A consequent next step would be to extend our within-colony approach to other colonies and compare the ecological circumstances under which bee-eaters reuse breeding burrows.

Population structure and phylogeography of the Gentoo Penguin (Pygoscelis papua) across the Scotia Arc.

PubMed

Levy, Hila; Clucas, Gemma V; Rogers, Alex D; Leaché, Adam D; Ciborowski, Kate L; Polito, Michael J; Lynch, Heather J; Dunn, Michael J; Hart, Tom

2016-03-01

Climate change, fisheries' pressure on penguin prey, and direct human disturbance of wildlife have all been implicated in causing large shifts in the abundance and distribution of penguins in the Southern Ocean. Without mark-recapture studies, understanding how colonies form and, by extension, how ranges shift is challenging. Genetic studies, particularly focused on newly established colonies, provide a snapshot of colonization and can reveal the extent to which shifts in abundance and occupancy result from changes in demographic rates (e.g., reproduction and survival) or migration among suitable patches of habitat. Here, we describe the population structure of a colonial seabird breeding across a large latitudinal range in the Southern Ocean. Using multilocus microsatellite genotype data from 510 Gentoo penguin (Pygoscelis papua) individuals from 14 colonies along the Scotia Arc and Antarctic Peninsula, together with mitochondrial DNA data, we find strong genetic differentiation between colonies north and south of the Polar Front, that coincides geographically with the taxonomic boundary separating the subspecies P. p. papua and P. p. ellsworthii. Using a discrete Bayesian phylogeographic approach, we show that southern Gentoos expanded from a possible glacial refuge in the center of their current range, colonizing regions to the north and south through rare, long-distance dispersal. Our findings show that this dispersal is important for new colony foundation and range expansion in a seabird species that ordinarily exhibits high levels of natal philopatry, though persistent oceanographic features serve as barriers to movement.

Conservation of colonial built heritage: practical considerations and cultural constraints in Rio de Janeiro

NASA Astrophysics Data System (ADS)

Smith, B.; Neto, J.; Silva, M.; Warke, P.; Curran, J.

2003-04-01

As the 500th anniversary of European arrival in Brazil approached in the year 2000, it provided a stimulus for the country to review the cultural and economic significance of its remaining colonial built heritage. This is not least because of the growing awareness that built heritage is an important attractor for high income tourism and an increasing willingness amongst the population at large to accept colonial artefacts as a legitimate component of national history. Nowhere is this revision more apposite than in the adjacent cities of Rio de Janeiro and Niteroi. In both cities much of the colonial heritage was swept away during the late twentieth century in a tide of reconstruction that was symbolic of self-proclaimed Brazilian modernity and that signified for many a break with their colonial past. Those elements of colonial heritage that have survived have done so largely because of their ownership either by the church or the military. However, whilst this has often protected the overall building, the detailed fabric of such structures has at best been neglected and in many cases abused. As a consequence, stonework, in particular, can exhibit a range of decay features that must be addressed if this heritage is to be preserved and its educational and economic potential realised. In this presentation, we review changing attitudes towards conservation as illustrated by a number of key structures, including the large stone forts that guard the entrance to Guanabara Bay. This is combined with a detailed examination of threats to the integrity of their stonework consequent on prolonged exposure in a humid tropical maritime environment. Most of these structures are built of local, very durable augen gneiss. However, studies of natural rock outcrops show that this rock does weather, and that breakdown can be episodic as localised strength thresholds are breached. Surveys suggest that some buildings may be approaching such threshold conditions, whereby stresses accumulated over centuries of apparent stability, combined with the effects of a more recent increase in atmospheric pollution, could trigger rapid breakdown. Management of this change will require either timely intervention to prevent a switch to rapid breakdown or potentially expensive restoration if warning signs are ignored.

Application of Metaheuristic and Deterministic Algorithms for Aircraft Reference Trajectory Optimization =

NASA Astrophysics Data System (ADS)

Murrieta Mendoza, Alejandro

Aircraft reference trajectory is an alternative method to reduce fuel consumption, thus the pollution released to the atmosphere. Fuel consumption reduction is of special importance for two reasons: first, because the aeronautical industry is responsible of 2% of the CO2 released to the atmosphere, and second, because it will reduce the flight cost. The aircraft fuel model was obtained from a numerical performance database which was created and validated by our industrial partner from flight experimental test data. A new methodology using the numerical database was proposed in this thesis to compute the fuel burn for a given trajectory. Weather parameters such as wind and temperature were taken into account as they have an important effect in fuel burn. The open source model used to obtain the weather forecast was provided by Weather Canada. A combination of linear and bi-linear interpolations allowed finding the required weather data. The search space was modelled using different graphs: one graph was used for mapping the different flight phases such as climb, cruise and descent, and another graph was used for mapping the physical space in which the aircraft would perform its flight. The trajectory was optimized in its vertical reference trajectory using the Beam Search algorithm, and a combination of the Beam Search algorithm with a search space reduction technique. The trajectory was optimized simultaneously for the vertical and lateral reference navigation plans while fulfilling a Required Time of Arrival constraint using three different metaheuristic algorithms: the artificial bee's colony, and the ant colony optimization. Results were validated using the software FlightSIMRTM, a commercial Flight Management System, an exhaustive search algorithm, and as flown flights obtained from flightawareRTM. All algorithms were able to reduce the fuel burn, and the flight costs. None None None None None None None

Optimization of human granulocyte macrophage-colony stimulating factor (hGM-CSF) expression using asparaginase and xylanase gene's signal sequences in Escherichia coli.

PubMed

Khasa, Yogender Pal; Khushoo, Amardeep; Tapryal, Suman; Mukherjee, K J

2011-09-01

The toxicity of the recombinant protein towards the expression host remains a significant deterrent for bioprocess development. In this study, the expression of human granulocyte macrophage-colony stimulating factor (hGM-CSF), which is known to be toxic to its host, was enhanced many folds using a combination of genetic and bioprocess strategies in Escherichia coli. The N terminus attachment of endoxylanase and asparaginase signal sequences from Bacillus subtilis and E. coli, respectively, in combination with and without His-tag, considerably improved expression levels. Induction and media optimization studies in shake flask cultures resulted in a maximal hGM-CSF concentration of 365 mg/L in the form of inclusion bodies (IBs) with a specific product yield (Y (P/X)) of 120 mg/g dry cell weight in case of the asparaginase signal. Culturing the cells in nutrient rich Terrific broth maintained the specific product yields (Y (P/X)) while a 6.6-fold higher volumetric concentration of both product and biomass was obtained. The purification and refolding steps were optimized resulting in a 95% pure protein with a fairly high refolding yield of 45%. The biological activity of the refolded protein was confirmed by a cell proliferation assay on hGM-CSF dependent human erythroleukemia TF-1 cells. This study demonstrated that this indeed is a viable route for the efficient production of hGM-CSF.

Forearm ischemia decreases endothelial colony-forming cell angiogenic potential.

PubMed

Mauge, Laetitia; Sabatier, Florence; Boutouyrie, Pierre; D'Audigier, Clément; Peyrard, Séverine; Bozec, Erwan; Blanchard, Anne; Azizi, Michel; Dizier, Blandine; Dignat-George, Françoise; Gaussem, Pascale; Smadja, David M

2014-02-01

Circulating endothelial progenitor cells and especially endothelial colony-forming cells (ECFCs) are promising candidate cells for endothelial regenerative medicine of ischemic diseases, but the conditions for an optimal collection from adult blood must be improved. On the basis of a recently reported vascular niche of ECFCs, we hypothesized that a local ischemia could trigger ECFC mobilization from the vascular wall into peripheral blood to optimize their collection for autologous implantation in critical leg ischemia. Because the target population with critical leg ischemia is composed of elderly patients in whom a vascular impairment has been documented, we also analyzed the impact of aging on ECFC mobilization and vascular integrity. After having defined optimized ECFC culture conditions, we studied the effect of forearm ischemia on ECFC numbers and functions in 26 healthy volunteers (13 volunteers ages 20-30-years old versus 13 volunteers ages 60-70 years old). The results show that forearm ischemia induced an efficient local ischemia and a normal endothelial response but did not mobilize ECFCs regardless of the age group. Moreover, we report an alteration of angiogenic properties of ECFCs obtained after forearm ischemia, in vitro as well as in vivo in a hindlimb ischemia murine model. This impaired ECFC angiogenic potential was not associated with a quantitative modification of the circulating endothelial compartment. The procedure of local ischemia, although reulting in a preserved endothelial reactivity, did not mobilize ECFCs but altered their angiogenic potential. Copyright © 2014 International Society for Cellular Therapy. Published by Elsevier Inc. All rights reserved.

Modeling man: the monkey colony at the Carnegie Institution of Washington's Department of Embryology, 1925-1971.

PubMed

Wilson, Emily K

2012-01-01

Though better recognized for its immediate endeavors in human embryo research, the Carnegie Department of Embryology also employed a breeding colony of rhesus macaques for the purposes of studying human reproduction. This essay follows the course of the first enterprise in maintaining a primate colony for laboratory research and the overlapping scientific, social, and political circumstances that tolerated and cultivated the colony's continued operation from 1925 until 1971. Despite a new-found priority for reproductive sciences in the United States, by the early 1920s an unfertilized human ovum had not yet been seen and even the timing of ovulation remained unresolved. Progress would require an organized research approach that could extend beyond the limitations of working with scant and inherently restrictive human subjects or with common lab mammals like mice. In response, the Department of Embryology, under the Carnegie Institution of Washington (CIW), instituted a novel methodology using a particular primate species as a surrogate in studying normal human reproductive physiology. Over more than 40 years the monkey colony followed an unpremeditated trajectory that would contribute fundamentally to discoveries in human reproduction, early embryo development, reliable birth control methods, and to the establishment of the rhesus macaque as a common model organism.

Differentiation of Mouse Ovarian Stem Cells Toward Oocyte-Like Structure by Coculture with Granulosa Cells.

PubMed

Parvari, Soraya; Yazdekhasti, Hossein; Rajabi, Zahra; Gerayeli Malek, Valliollah; Rastegar, Tayebeh; Abbasi, Mehdi

2016-11-01

An increasing body of evidence has confirmed existence and function of ovarian stem cells (OSCs). In this study, a novel approach on differentiation of OSCs into oocyte-like cells (OLCs) has been addressed. Recently, different methods have been recruited to isolate and describe aspects of OSCs, but newer and more convenient strategies in isolation are still growing. Herein, a morphology-based method was used to isolate OSCs. Cell suspension of mouse neonatal ovaries was cultured and formed colonies were harvested mechanically and cultivated on mouse embryonic fibroblasts. For differentiation induction, colonies transferred on inactive granulosa cells. Results showed that cells in colonies were positive for alkaline phosphatase activity and reverse transcription-polymerase chain reaction (RT-PCR) confirmed the pluripotency characteristics of cells. Immunofluorescence revealed a positive signal for OCT4, DAZL, MVH, and SSEA1 in colonies as well. Results of RT-PCR and immunofluorescence confirmed that some OLCs were generated within the germ stem cell (GSCs) colonies. The applicability of morphological selection for isolation of GSCs was verified. This method is easier and more economic than other techniques. Our results demonstrate that granulosa cells were effective in inducing the differentiation of OSCs into OLCs through direct cell-to-cell contacts.

Collagen gel droplet-embedded culture drug sensitivity testing in squamous cell carcinoma cell lines derived from human oral cancers: Optimal contact concentrations of cisplatin and fluorouracil.

PubMed

Sakuma, Kaname; Tanaka, Akira; Mataga, Izumi

2016-12-01

The collagen gel droplet-embedded culture drug sensitivity test (CD-DST) is an anticancer drug sensitivity test that uses a method of three-dimensional culture of extremely small samples, and it is suited to primary cultures of human cancer cells. It is a useful method for oral squamous cell carcinoma (OSCC), in which the cancer tissues available for testing are limited. However, since the optimal contact concentrations of anticancer drugs have yet to be established in OSCC, CD-DST for detecting drug sensitivities of OSCC is currently performed by applying the optimal contact concentrations for stomach cancer. In the present study, squamous carcinoma cell lines from human oral cancer were used to investigate the optimal contact concentrations of cisplatin (CDDP) and fluorouracil (5-FU) during CD-DST for OSCC. CD-DST was performed in 7 squamous cell carcinoma cell lines derived from human oral cancers (Ca9-22, HSC-3, HSC-4, HO-1-N-1, KON, OSC-19 and SAS) using CDDP (0.15, 0.3, 1.25, 2.5, 5.0 and 10.0 µg/ml) and 5-FU (0.4, 0.9, 1.8, 3.8, 7.5, 15.0 and 30.0 µg/ml), and the optimal contact concentrations were calculated from the clinical response rate of OSCC to single-drug treatment and the in vitro efficacy rate curve. The optimal concentrations were 0.5 µg/ml for CDDP and 0.7 µg/ml for 5-FU. The antitumor efficacy of CDDP at this optimal contact concentration in CD-DST was compared to the antitumor efficacy in the nude mouse method. The T/C values, which were calculated as the ratio of the colony volume of the treatment group and the colony volume of the control group, at the optimal contact concentration of CDDP and of the nude mouse method were almost in agreement (P<0.05) and predicted clinical efficacy, indicating that the calculated optimal contact concentration is valid. Therefore, chemotherapy for OSCC based on anticancer drug sensitivity tests offers patients a greater freedom of choice and is likely to assume a greater importance in the selection of treatment from the perspectives of function preservation and quality of life, as well as representing a treatment option for unresectable, intractable or recurrent cases.

New approach for determination of an optimum honeybee colony's carrying capacity based on productivity and nectar secretion potential of bee forage species.

PubMed

Al-Ghamdi, Ahmed; Adgaba, Nuru; Getachew, Awraris; Tadesse, Yilma

2016-01-01

The present study was carried out to determine an optimum honeybee colony's carrying capacity of selected valleys dominated by Ziziphus spina-christi and Acacia tortilis in the Al-Baha region, Kingdom of Saudi Arabia. The study was conducted based on the assessment of the number of colonies kept, their productivities and the existing productive bee forage resources in the target valleys with its economic implication. In the existing beekeeping practice, the average number of managed honeybee colonies introduced per square kilometer was 530 and 317 during the flowering period of Z. spina-christi and A. tortilis, respectively. Furthermore, the overall ratios of productive bee forage plants to the number of honeybee colonies introduced were 0.55 and 11.12 to Ziziphus trees and A. tortilis shrubs respectively. In the existing situation the average honey production potential of 5.21 and 0.34 kg was recorded per Ziziphus and A. tortilis plants per flowering season, respectively. The present study, revealed that the number of honeybee colonies introduced in relation to the existing bee forage potential was extremely overcrowding which is beyond the carrying capacity of bee forage resources in selected valleys and it has been observed to affect the productivities and subsequent profitability of beekeeping. The study infers that, by keeping the optimum honeybee colony's carrying capacity of valleys (88 traditional hives/km(2) or 54 Langstroth hives/km(2) in Ziziphus field and 72 traditional hives/km(2) or 44 Langstroth hives/km(2) in A. tortilis field), profitability of beekeeping can be boosted up to 130.39% and 207.98% during Z. spina-christi and A. tortilis, flowering seasons, respectively.

Research on schedulers for astronomical observatories

NASA Astrophysics Data System (ADS)

Colome, Josep; Colomer, Pau; Guàrdia, Josep; Ribas, Ignasi; Campreciós, Jordi; Coiffard, Thierry; Gesa, Lluis; Martínez, Francesc; Rodler, Florian

2012-09-01

The main task of a scheduler applied to astronomical observatories is the time optimization of the facility and the maximization of the scientific return. Scheduling of astronomical observations is an example of the classical task allocation problem known as the job-shop problem (JSP), where N ideal tasks are assigned to M identical resources, while minimizing the total execution time. A problem of higher complexity, called the Flexible-JSP (FJSP), arises when the tasks can be executed by different resources, i.e. by different telescopes, and it focuses on determining a routing policy (i.e., which machine to assign for each operation) other than the traditional scheduling decisions (i.e., to determine the starting time of each operation). In most cases there is no single best approach to solve the planning system and, therefore, various mathematical algorithms (Genetic Algorithms, Ant Colony Optimization algorithms, Multi-Objective Evolutionary algorithms, etc.) are usually considered to adapt the application to the system configuration and task execution constraints. The scheduling time-cycle is also an important ingredient to determine the best approach. A shortterm scheduler, for instance, has to find a good solution with the minimum computation time, providing the system with the capability to adapt the selected task to varying execution constraints (i.e., environment conditions). We present in this contribution an analysis of the task allocation problem and the solutions currently in use at different astronomical facilities. We also describe the schedulers for three different projects (CTA, CARMENES and TJO) where the conclusions of this analysis are applied to develop a suitable routine.

Historical trauma: politics of a conceptual framework.

PubMed

Prussing, Erica

2014-06-01

The concept of historical trauma (HT) is compelling: Colonialism has set forth cumulative cycles of adversity that promote morbidity and mortality at personal and collective levels, with especially strong mental health impacts. Yet as ongoing community-based as well as scholarly discussions attest, lingering questions continue to surround HT as a framework for understanding the relationships between colonialism and indigenous mental health. Through an overview of 30 recent peer-reviewed publications that aim to clarify, define, measure, and interpret how HT impacts American Indian and Alaska Native (AIAN) mental health, this paper examines how the conceptual framework of HT has circulated in ways shaped by interactions among three prominent research approaches: evidence-based, culturally relevant, and decolonizing. All define current approaches to AIAN mental health research, but each sets forth different conceptualizations of the connections between colonialism and psychological distress. The unfolding trajectory of research about HT reflects persistent tensions in how these frameworks interact, but also possibilities for better integrating them. These considerations aim to advance conversations about the politics of producing knowledge about AIAN mental health, and support ongoing calls for greater political pluralism in mental health research. © The Author(s) 2014 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

Health Evaluation of Experimental Laboratory Mice.

PubMed

Burkholder, Tanya; Foltz, Charmaine; Karlsson, Eleanor; Linton, C Garry; Smith, Joanne M

2012-06-01

Good science and good animal care go hand in hand. A sick or distressed animal does not produce the reliable results that a healthy and unstressed animal produces. This unit describes the essentials of assessing mouse health, colony health surveillance, common conditions, and determination of appropriate endpoints. Understanding the health and well-being of the mice used in research enables the investigator to optimize research results and animal care.

Optimists or realists? How ants allocate resources in making reproductive investments.

PubMed

Enzmann, Brittany L; Nonacs, Peter

2018-04-24

Parents often face an investment trade-off between either producing many small or fewer large offspring. When environments vary predictably, the fittest parental solution matches available resources by varying only number of offspring and never optimal individual size. However when mismatches occur often between parental expectations and true resource levels, dynamic models like multifaceted parental investment (MFPI) and parental optimism (PO) both predict offspring size can vary significantly. MFPI is a "realist" strategy: parents assume future environments of average richness. When resources exceed expectations and it is too late to add more offspring, the best-case solution increases investment per individual. Brood size distributions therefore track the degree of mismatch from right-skewed around an optimal size (slight underestimation of resources) to left-skewed around a maximal size (gross underestimation). Conversely, PO is an "optimist" strategy: parents assume maximally good resource futures and match numbers to that situation. Normal or lean years do not affect "core" brood as costs primarily fall on excess "marginal" siblings who die or experience stunted growth (producing left-skewed distributions). Investment patterns supportive of both MFPI and PO models have been observed in nature, but studies that directly manipulate food resources to test predictions are lacking. Ant colonies produce many offspring per reproductive cycle and are amenable to experimental manipulation in ways that can differentiate between MFPI and PO investment strategies. Colonies in a natural population of a harvester ant (Pogonomyrmex salinus) were protein-supplemented over 2 years, and mature sexual offspring were collected annually prior to their nuptial flight. Several results support either MFPI or PO in terms of patterns in offspring size distributions and how protein differentially affected male and female production. Unpredicted by either model, however, is that supplementation affected distributions more strongly across years than within (e.g., small females are significantly rarer in the year after colonies receive protein). Parental investment strategies in P. salinus vary dynamically across years and conditions. Finding that past conditions can more strongly affect reproductive decisions than current ones, however, is not addressed by models of parental investment. © 2018 The Authors. Journal of Animal Ecology © 2018 British Ecological Society.

Space colonies and energy supply to the earth

NASA Technical Reports Server (NTRS)

Oneill, G. K.

1975-01-01

It is pointed out that a space manufacturing facility may be economically more effective than alternative industries on the earth for the construction of products which are to be used in geosynchronous or higher orbits. The suggestion is made to construct solar power stations at a space colony and relocate them in geosynchronous orbit to supply energy to the earth. Attention is given to energy problems and approaches for solving them, taking into account environmental effects and economic factors. Economic aspects of space manufacturing are discussed in some detail.

Multitrophic interaction facilitates parasite-host relationship between an invasive beetle and the honey bee.

PubMed

Torto, Baldwyn; Boucias, Drion G; Arbogast, Richard T; Tumlinson, James H; Teal, Peter E A

2007-05-15

Colony defense by honey bees, Apis mellifera, is associated with stinging and mass attack, fueled by the release of alarm pheromones. Thus, alarm pheromones are critically important to survival of honey bee colonies. Here we report that in the parasitic relationship between the European honey bee and the small hive beetle, Aethina tumida, the honey bee's alarm pheromones serve a negative function because they are potent attractants for the beetle. Furthermore, we discovered that the beetles from both Africa and the United States vector a strain of Kodamaea ohmeri yeast, which produces these same honey bee alarm pheromones when grown on pollen in hives. The beetle is not a pest of African honey bees because African bees have evolved effective methods to mitigate beetle infestation. However, European honey bees, faced with disease and pest management stresses different from those experienced by African bees, are unable to effectively inhibit beetle infestation. Therefore, the environment of the European honey bee colony provides optimal conditions to promote the unique bee-beetle-yeast-pollen multitrophic interaction that facilitates effective infestation of hives at the expense of the European honey bee.

Developmental insights from early mammalian embryos and core signaling pathways that influence human pluripotent cell growth and differentiation.

PubMed

Chen, Kevin G; Mallon, Barbara S; Johnson, Kory R; Hamilton, Rebecca S; McKay, Ronald D G; Robey, Pamela G

2014-05-01

Human pluripotent stem cells (hPSCs) have two potentially attractive applications: cell replacement-based therapies and drug discovery. Both require the efficient generation of large quantities of clinical-grade stem cells that are free from harmful genomic alterations. The currently employed colony-type culture methods often result in low cell yields, unavoidably heterogeneous cell populations, and substantial chromosomal abnormalities. Here, we shed light on the structural relationship between hPSC colonies/embryoid bodies and early-stage embryos in order to optimize current culture methods based on the insights from developmental biology. We further highlight core signaling pathways that underlie multiple epithelial-to-mesenchymal transitions (EMTs), cellular heterogeneity, and chromosomal instability in hPSCs. We also analyze emerging methods such as non-colony type monolayer (NCM) and suspension culture, which provide alternative growth models for hPSC expansion and differentiation. Furthermore, based on the influence of cell-cell interactions and signaling pathways, we propose concepts, strategies, and solutions for production of clinical-grade hPSCs, stem cell precursors, and miniorganoids, which are pivotal steps needed for future clinical applications. Published by Elsevier B.V.

Live-cell imaging of conidial anastomosis tube fusion during colony initiation in Fusarium oxysporum

PubMed Central

Kurian, Smija M.; Di Pietro, Antonio

2018-01-01

Fusarium oxysporum exhibits conidial anastomosis tube (CAT) fusion during colony initiation to form networks of conidial germlings. Here we determined the optimal culture conditions for this fungus to undergo CAT fusion between microconidia in liquid medium. Extensive high resolution, confocal live-cell imaging was performed to characterise the different stages of CAT fusion, using genetically encoded fluorescent labelling and vital fluorescent organelle stains. CAT homing and fusion were found to be dependent on adhesion to the surface, in contrast to germ tube development which occurs in the absence of adhesion. Staining with fluorescently labelled concanavalin A indicated that the cell wall composition of CATs differs from that of microconidia and germ tubes. The movement of nuclei, mitochondria, vacuoles and lipid droplets through fused germlings was observed by live-cell imaging. PMID:29734342

Live-cell imaging of conidial anastomosis tube fusion during colony initiation in Fusarium oxysporum.

PubMed

Kurian, Smija M; Di Pietro, Antonio; Read, Nick D

2018-01-01

Fusarium oxysporum exhibits conidial anastomosis tube (CAT) fusion during colony initiation to form networks of conidial germlings. Here we determined the optimal culture conditions for this fungus to undergo CAT fusion between microconidia in liquid medium. Extensive high resolution, confocal live-cell imaging was performed to characterise the different stages of CAT fusion, using genetically encoded fluorescent labelling and vital fluorescent organelle stains. CAT homing and fusion were found to be dependent on adhesion to the surface, in contrast to germ tube development which occurs in the absence of adhesion. Staining with fluorescently labelled concanavalin A indicated that the cell wall composition of CATs differs from that of microconidia and germ tubes. The movement of nuclei, mitochondria, vacuoles and lipid droplets through fused germlings was observed by live-cell imaging.

Optimization of culture conditions to support long-term self-renewal of buffalo (Bubalus bubalis) embryonic stem cell-like cells.

PubMed

Sharma, Ruchi; George, Aman; Kamble, Nitin Manchindra; Singh, Karn Pratap; Chauhan, Manmohan Singh; Singla, Suresh Kumar; Manik, Radhey Sham; Palta, Prabhat

2011-12-01

A culture system capable of sustaining self-renewal of buffalo embryonic stem (ES) cell-like cells in an undifferentiated state over a long period of time was developed. Inner cell masses were seeded on KO-DMEM+15% KO-serum replacer on buffalo fetal fibroblast feeder layer. Supplementation of culture medium with 5 ng/mL FGF-2 and 1000 IU/mL mLIF gave the highest (p<0.05) rate of primary colony formation. The ES cell-like cells' colony survival rate and increase in colony size were highest (p<0.05) following supplementation with FGF-2 and LIF compared to other groups examined. FGF-2 supplementation affected the quantitative expression of NANOG, SOX-2, ACTIVIN A, BMP 4, and TGFβ1, but not OCT4 and GREMLIN. Supplementation with SU5402, an FGFR inhibitor (≥20 μM) increased (p<0.05) the percentage of colonies that differentiated. FGFR1-3 and ERK1, K-RAS, E-RAS, and SHP-2, key signaling intermediates of FGF signaling, were detected in ES cell-like cells. Under culture conditions described, three ES cell lines were derived that, to date, have been maintained for 135, 95, and 85 passages for over 27, 19, and 17 months, respectively, whereas under other conditions examined, ES cell-like cells did not survive beyond passage 10. The ES cell-like cells were regularly monitored for expression of pluripotency markers and their potency to form embryoid bodies.

Solving Energy-Aware Real-Time Tasks Scheduling Problem with Shuffled Frog Leaping Algorithm on Heterogeneous Platforms

PubMed Central

Zhang, Weizhe; Bai, Enci; He, Hui; Cheng, Albert M.K.

2015-01-01

Reducing energy consumption is becoming very important in order to keep battery life and lower overall operational costs for heterogeneous real-time multiprocessor systems. In this paper, we first formulate this as a combinatorial optimization problem. Then, a successful meta-heuristic, called Shuffled Frog Leaping Algorithm (SFLA) is proposed to reduce the energy consumption. Precocity remission and local optimal avoidance techniques are proposed to avoid the precocity and improve the solution quality. Convergence acceleration significantly reduces the search time. Experimental results show that the SFLA-based energy-aware meta-heuristic uses 30% less energy than the Ant Colony Optimization (ACO) algorithm, and 60% less energy than the Genetic Algorithm (GA) algorithm. Remarkably, the running time of the SFLA-based meta-heuristic is 20 and 200 times less than ACO and GA, respectively, for finding the optimal solution. PMID:26110406

Fuzzy Controller Design Using Evolutionary Techniques for Twin Rotor MIMO System: A Comparative Study.

PubMed

Hashim, H A; Abido, M A

2015-01-01

This paper presents a comparative study of fuzzy controller design for the twin rotor multi-input multioutput (MIMO) system (TRMS) considering most promising evolutionary techniques. These are gravitational search algorithm (GSA), particle swarm optimization (PSO), artificial bee colony (ABC), and differential evolution (DE). In this study, the gains of four fuzzy proportional derivative (PD) controllers for TRMS have been optimized using the considered techniques. The optimization techniques are developed to identify the optimal control parameters for system stability enhancement, to cancel high nonlinearities in the model, to reduce the coupling effect, and to drive TRMS pitch and yaw angles into the desired tracking trajectory efficiently and accurately. The most effective technique in terms of system response due to different disturbances has been investigated. In this work, it is observed that GSA is the most effective technique in terms of solution quality and convergence speed.

Fuzzy Controller Design Using Evolutionary Techniques for Twin Rotor MIMO System: A Comparative Study

PubMed Central

Hashim, H. A.; Abido, M. A.

2015-01-01

This paper presents a comparative study of fuzzy controller design for the twin rotor multi-input multioutput (MIMO) system (TRMS) considering most promising evolutionary techniques. These are gravitational search algorithm (GSA), particle swarm optimization (PSO), artificial bee colony (ABC), and differential evolution (DE). In this study, the gains of four fuzzy proportional derivative (PD) controllers for TRMS have been optimized using the considered techniques. The optimization techniques are developed to identify the optimal control parameters for system stability enhancement, to cancel high nonlinearities in the model, to reduce the coupling effect, and to drive TRMS pitch and yaw angles into the desired tracking trajectory efficiently and accurately. The most effective technique in terms of system response due to different disturbances has been investigated. In this work, it is observed that GSA is the most effective technique in terms of solution quality and convergence speed. PMID:25960738

Non-uniform cosine modulated filter banks using meta-heuristic algorithms in CSD space.

PubMed

Kalathil, Shaeen; Elias, Elizabeth

2015-11-01

This paper presents an efficient design of non-uniform cosine modulated filter banks (CMFB) using canonic signed digit (CSD) coefficients. CMFB has got an easy and efficient design approach. Non-uniform decomposition can be easily obtained by merging the appropriate filters of a uniform filter bank. Only the prototype filter needs to be designed and optimized. In this paper, the prototype filter is designed using window method, weighted Chebyshev approximation and weighted constrained least square approximation. The coefficients are quantized into CSD, using a look-up-table. The finite precision CSD rounding, deteriorates the filter bank performances. The performances of the filter bank are improved using suitably modified meta-heuristic algorithms. The different meta-heuristic algorithms which are modified and used in this paper are Artificial Bee Colony algorithm, Gravitational Search algorithm, Harmony Search algorithm and Genetic algorithm and they result in filter banks with less implementation complexity, power consumption and area requirements when compared with those of the conventional continuous coefficient non-uniform CMFB.

Non-uniform cosine modulated filter banks using meta-heuristic algorithms in CSD space

PubMed Central

Kalathil, Shaeen; Elias, Elizabeth

2014-01-01

This paper presents an efficient design of non-uniform cosine modulated filter banks (CMFB) using canonic signed digit (CSD) coefficients. CMFB has got an easy and efficient design approach. Non-uniform decomposition can be easily obtained by merging the appropriate filters of a uniform filter bank. Only the prototype filter needs to be designed and optimized. In this paper, the prototype filter is designed using window method, weighted Chebyshev approximation and weighted constrained least square approximation. The coefficients are quantized into CSD, using a look-up-table. The finite precision CSD rounding, deteriorates the filter bank performances. The performances of the filter bank are improved using suitably modified meta-heuristic algorithms. The different meta-heuristic algorithms which are modified and used in this paper are Artificial Bee Colony algorithm, Gravitational Search algorithm, Harmony Search algorithm and Genetic algorithm and they result in filter banks with less implementation complexity, power consumption and area requirements when compared with those of the conventional continuous coefficient non-uniform CMFB. PMID:26644921

Comparison and assessment of aerial and ground estimates of waterbird colonies

USGS Publications Warehouse

Green, M.C.; Luent, M.C.; Michot, T.C.; Jeske, C.W.; Leberg, P.L.

2008-01-01

Aerial surveys are often used to quantify sizes of waterbird colonies; however, these surveys would benefit from a better understanding of associated biases. We compared estimates of breeding pairs of waterbirds, in colonies across southern Louisiana, USA, made from the ground, fixed-wing aircraft, and a helicopter. We used a marked-subsample method for ground-counting colonies to obtain estimates of error and visibility bias. We made comparisons over 2 sampling periods: 1) surveys conducted on the same colonies using all 3 methods during 3-11 May 2005 and 2) an expanded fixed-wing and ground-survey comparison conducted over 4 periods (May and Jun, 2004-2005). Estimates from fixed-wing aircraft were approximately 65% higher than those from ground counts for overall estimated number of breeding pairs and for both dark and white-plumaged species. The coefficient of determination between estimates based on ground and fixed-wing aircraft was ???0.40 for most species, and based on the assumption that estimates from the ground were closer to the true count, fixed-wing aerial surveys appeared to overestimate numbers of nesting birds of some species; this bias often increased with the size of the colony. Unlike estimates from fixed-wing aircraft, numbers of nesting pairs made from ground and helicopter surveys were very similar for all species we observed. Ground counts by one observer resulted in underestimated number of breeding pairs by 20% on average. The marked-subsample method provided an estimate of the number of missed nests as well as an estimate of precision. These estimates represent a major advantage of marked-subsample ground counts over aerial methods; however, ground counts are difficult in large or remote colonies. Helicopter surveys and ground counts provide less biased, more precise estimates of breeding pairs than do surveys made from fixed-wing aircraft. We recommend managers employ ground counts using double observers for surveying waterbird colonies when feasible. Fixed-wing aerial surveys may be suitable to determine colony activity and composition of common waterbird species. The most appropriate combination of survey approaches will be based on the need for precise and unbiased estimates, balanced with financial and logistical constraints.

Carbonate precipitation under bulk acidic conditions as a potential biosignature for searching life on Mars

NASA Astrophysics Data System (ADS)

Fernández-Remolar, David C.; Preston, Louisa J.; Sánchez-Román, Mónica; Izawa, Matthew R. M.; Huang, L.; Southam, Gordon; Banerjee, Neil R.; Osinski, Gordon R.; Flemming, Roberta; Gómez-Ortíz, David; Prieto Ballesteros, Olga; Rodríguez, Nuria; Amils, Ricardo; Darby Dyar, M.

2012-10-01

Recent observations of carbonate minerals in ancient Martian rocks have been interpreted as evidence for the former presence of circumneutral solutions optimal for carbonate precipitation. Sampling from surface and subsurface regions of the low-pH system of Río Tinto has shown, unexpectedly, that carbonates can form under diverse macroscopic physicochemical conditions ranging from very low to neutral pH (1.5-7.0). A multi-technique approach demonstrates that carbonate minerals are closely associated with microbial activity. Carbonates occur in the form of micron-size carbonate precipitates under bacterial biofilms, mineralization of subsurface colonies, and possible biogenic microstructures including globules, platelets and dumbbell morphologies. We propose that carbonate precipitation in the low-pH environment of Río Tinto is a process enabled by microbially-mediated neutralization driven by the reduction of ferric iron coupled to the oxidation of biomolecules in microbially-maintained circumneutral oases, where the local pH (at the scale of cells or cell colonies) can be much different than in the macroscopic environment. Acidic conditions were likely predominant in vast regions of Mars over the last four billion years of planetary evolution. Ancient Martian microbial life inhabiting low-pH environments could have precipitated carbonates similar to those observed at Río Tinto. Preservation of carbonates at Río Tinto over geologically significant timescales suggests that similarly-formed carbonate minerals could also be preserved on Mars. Such carbonates could soon be observed by the Mars Science Laboratory, and by future missions to the red planet.

Pharmacokinetic and pharmacodynamic modelling of the novel human granulocyte colony-stimulating factor derivative Maxy-G34 and pegfilgrastim in rats.

PubMed

Scholz, M; Engel, C; Apt, D; Sankar, S L; Goldstein, E; Loeffler, M

2009-12-01

This study aims to compare pharmacokinetics and pharmacodynamics of pegfilgrastim, a pharmaceutical recombinant human granulocyte colony-stimulating factor (rhG-CSF), with that of a newly developed reagent, Maxy-G34. This comparison was performed using rat experiments and biomathematical modelling of granulopoiesis. Healthy rats and those with cyclophosphamide-induced neutropenia were treated with either pegfilgrastim or Maxy-G34 under various schedules. Time courses of absolute neutrophil count (ANC) and G-CSF serum level were measured and we constructed a combined pharmacokinetic/pharmacodynamic model of both drugs. Neutropenic episodes were assessed by experimental data and model simulations. Both Pegfilgrastim and Maxy-G34 showed strong dose-dependent efficacy in reducing neutropenic episodes. However, time courses of ANC and G-CSF serum levels were markedly different. The biomathematical model showed good agreement with these data. We estimated that differences between the two drugs could be explained by lower bioavailability and reduced elimination of Maxy-G34. Based on the data and model interpolations, we estimated that Maxy-G34 is superior in reducing neutropenic episodes. Also, we predicted that G-CSF administration 48 h after cyclophosphamide would be superior to its administration after 2 or 24 h, for both derivatives. Maxy-G34 is a highly potent drug for stimulation of neutrophil production in rats. By our modelling approach, we quantified differences between Maxy-G34 and pegfilgrastim, related to pharmacokinetic parameters. Model simulations can be used to estimate optimal dosing and timing options in the present preclinical rat model.

A multi-group firefly algorithm for numerical optimization

NASA Astrophysics Data System (ADS)

Tong, Nan; Fu, Qiang; Zhong, Caiming; Wang, Pengjun

2017-08-01

To solve the problem of premature convergence of firefly algorithm (FA), this paper analyzes the evolution mechanism of the algorithm, and proposes an improved Firefly algorithm based on modified evolution model and multi-group learning mechanism (IMGFA). A Firefly colony is divided into several subgroups with different model parameters. Within each subgroup, the optimal firefly is responsible for leading the others fireflies to implement the early global evolution, and establish the information mutual system among the fireflies. And then, each firefly achieves local search by following the brighter firefly in its neighbors. At the same time, learning mechanism among the best fireflies in various subgroups to exchange information can help the population to obtain global optimization goals more effectively. Experimental results verify the effectiveness of the proposed algorithm.

Spatial structure and nest demography reveal the influence of competition, parasitism and habitat quality on slavemaking ants and their hosts

PubMed Central

2011-01-01

Background Natural communities are structured by intra-guild competition, predation or parasitism and the abiotic environment. We studied the relative importance of these factors in two host-social parasite ecosystems in three ant communities in Europe (Bavaria) and North America (New York, West Virginia). We tested how these factors affect colony demography, life-history and the spatial pattern of colonies, using a large sample size of more than 1000 colonies. The strength of competition was measured by the distance to the nearest competitor. Distance to the closest social parasite colony was used as a measure of parasitism risk. Nest sites (i.e., sticks or acorns) are limited in these forest ecosystems and we therefore included nest site quality as an abiotic factor in the analysis. In contrast to previous studies based on local densities, we focus here on the positioning and spatial patterns and we use models to compare our predictions to random expectations. Results Colony demography was universally affected by the size of the nest site with larger and more productive colonies residing in larger nest sites of higher quality. Distance to the nearest competitor negatively influenced host demography and brood production in the Bavarian community, pointing to an important role of competition, while social parasitism was less influential in this community. The New York community was characterized by the highest habitat variability, and productive colonies were clustered in sites of higher quality. Colonies were clumped on finer spatial scales, when we considered only the nearest neighbors, but more regularly distributed on coarser scales. The analysis of spatial positioning within plots often produced different results compared to those based on colony densities. For example, while host and slavemaker densities are often positively correlated, slavemakers do not nest closer to potential host colonies than expected by random. Conclusions The three communities are differently affected by biotic and abiotic factors. Some of the differences can be attributed to habitat differences and some to differences between the two slavemaking-host ecosystems. The strong effect of competition in the Bavarian community points to the scarcity of resources in this uniform habitat compared to the other more diverse sites. The decrease in colony aggregation with scale indicates fine-scale resource hotspots: colonies are locally aggregated in small groups. Our study demonstrates that species relationships vary across scales and spatial patterns can provide important insights into species interactions. These results could not have been obtained with analyses based on local densities alone. Previous studies focused on social parasitism and its effect on host colonies. The broader approach taken here, considering several possible factors affecting colony demography and not testing each one in isolation, shows that competition and environmental variability can have a similar strong impact on demography and life-history of hosts. We conclude that the effects of parasites or predators should be studied in parallel to other ecological influences. PMID:21443778

Spatial structure and nest demography reveal the influence of competition, parasitism and habitat quality on slavemaking ants and their hosts.

PubMed

Scharf, Inon; Fischer-Blass, Birgit; Foitzik, Susanne

2011-03-28

Natural communities are structured by intra-guild competition, predation or parasitism and the abiotic environment. We studied the relative importance of these factors in two host-social parasite ecosystems in three ant communities in Europe (Bavaria) and North America (New York, West Virginia). We tested how these factors affect colony demography, life-history and the spatial pattern of colonies, using a large sample size of more than 1000 colonies. The strength of competition was measured by the distance to the nearest competitor. Distance to the closest social parasite colony was used as a measure of parasitism risk. Nest sites (i.e., sticks or acorns) are limited in these forest ecosystems and we therefore included nest site quality as an abiotic factor in the analysis. In contrast to previous studies based on local densities, we focus here on the positioning and spatial patterns and we use models to compare our predictions to random expectations. Colony demography was universally affected by the size of the nest site with larger and more productive colonies residing in larger nest sites of higher quality. Distance to the nearest competitor negatively influenced host demography and brood production in the Bavarian community, pointing to an important role of competition, while social parasitism was less influential in this community. The New York community was characterized by the highest habitat variability, and productive colonies were clustered in sites of higher quality. Colonies were clumped on finer spatial scales, when we considered only the nearest neighbors, but more regularly distributed on coarser scales. The analysis of spatial positioning within plots often produced different results compared to those based on colony densities. For example, while host and slavemaker densities are often positively correlated, slavemakers do not nest closer to potential host colonies than expected by random. The three communities are differently affected by biotic and abiotic factors. Some of the differences can be attributed to habitat differences and some to differences between the two slavemaking-host ecosystems. The strong effect of competition in the Bavarian community points to the scarcity of resources in this uniform habitat compared to the other more diverse sites. The decrease in colony aggregation with scale indicates fine-scale resource hotspots: colonies are locally aggregated in small groups. Our study demonstrates that species relationships vary across scales and spatial patterns can provide important insights into species interactions. These results could not have been obtained with analyses based on local densities alone. Previous studies focused on social parasitism and its effect on host colonies. The broader approach taken here, considering several possible factors affecting colony demography and not testing each one in isolation, shows that competition and environmental variability can have a similar strong impact on demography and life-history of hosts. We conclude that the effects of parasites or predators should be studied in parallel to other ecological influences.

Design and implementation of intelligent electronic warfare decision making algorithm

NASA Astrophysics Data System (ADS)

Peng, Hsin-Hsien; Chen, Chang-Kuo; Hsueh, Chi-Shun

2017-05-01

Electromagnetic signals and the requirements of timely response have been a rapid growth in modern electronic warfare. Although jammers are limited resources, it is possible to achieve the best electronic warfare efficiency by tactical decisions. This paper proposes the intelligent electronic warfare decision support system. In this work, we develop a novel hybrid algorithm, Digital Pheromone Particle Swarm Optimization, based on Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) and Shuffled Frog Leaping Algorithm (SFLA). We use PSO to solve the problem and combine the concept of pheromones in ACO to accumulate more useful information in spatial solving process and speed up finding the optimal solution. The proposed algorithm finds the optimal solution in reasonable computation time by using the method of matrix conversion in SFLA. The results indicated that jammer allocation was more effective. The system based on the hybrid algorithm provides electronic warfare commanders with critical information to assist commanders in effectively managing the complex electromagnetic battlefield.

Freeze drying formulation using microscale and design of experiment approaches: a case study using granulocyte colony-stimulating factor.

PubMed

Grant, Yitzchak; Matejtschuk, Paul; Bird, Christopher; Wadhwa, Meenu; Dalby, Paul A

2012-04-01

The lyophilization of proteins in microplates, to assess and optimise formulations rapidly, has been applied for the first time to a therapeutic protein and, in particular, one that requires a cell-based biological assay, in order to demonstrate the broader usefulness of the approach. Factorial design of experiment methods were combined with lyophilization in microplates to identify optimum formulations that stabilised granulocyte colony-stimulating factor during freeze drying. An initial screen rapidly identified key excipients and potential interactions, which was then followed by a central composite face designed optimisation experiment. Human serum albumin and Tween 20 had significant effects on maintaining protein stability. As previously, the optimum formulation was then freeze-dried in stoppered vials to verify that the microscale data is relevant to pilot scales. However, to validate the approach further, the selected formulation was also assessed for solid-state shelf-life through the use of accelerated stability studies. This approach allows for a high-throughput assessment of excipient options early on in product development, while also reducing costs in terms of time and quantity of materials required.

Image-based quantification and mathematical modeling of spatial heterogeneity in ESC colonies.

PubMed

Herberg, Maria; Zerjatke, Thomas; de Back, Walter; Glauche, Ingmar; Roeder, Ingo

2015-06-01

Pluripotent embryonic stem cells (ESCs) have the potential to differentiate into cells of all three germ layers. This unique property has been extensively studied on the intracellular, transcriptional level. However, ESCs typically form clusters of cells with distinct size and shape, and establish spatial structures that are vital for the maintenance of pluripotency. Even though it is recognized that the cells' arrangement and local interactions play a role in fate decision processes, the relations between transcriptional and spatial patterns have not yet been studied. We present a systems biology approach which combines live-cell imaging, quantitative image analysis, and multiscale, mathematical modeling of ESC growth. In particular, we develop quantitative measures of the morphology and of the spatial clustering of ESCs with different expression levels and apply them to images of both in vitro and in silico cultures. Using the same measures, we are able to compare model scenarios with different assumptions on cell-cell adhesions and intercellular feedback mechanisms directly with experimental data. Applying our methodology to microscopy images of cultured ESCs, we demonstrate that the emerging colonies are highly variable regarding both morphological and spatial fluorescence patterns. Moreover, we can show that most ESC colonies contain only one cluster of cells with high self-renewing capacity. These cells are preferentially located in the interior of a colony structure. The integrated approach combining image analysis with mathematical modeling allows us to reveal potential transcription factor related cellular and intercellular mechanisms behind the emergence of observed patterns that cannot be derived from images directly. © 2015 International Society for Advancement of Cytometry.

[Recombinant granulocyte-colony stimulating factor (filgrastim): optimization of conditions of isolation and purification from inclusion body].

PubMed

Kononova, N V; Iakovlev, A V; Zhuravko, A M; Pankeev, N N; Minaev, S V; Bobruskin, A I; Mart'ianov, V A

2014-01-01

We developed a unified process platform for two recombinant human GCSF medicines--one with the non-prolonged and the other with prolonged action. This unified technology led to a simpler and cheaper production while introduction of the additional pegylation stage to the technological line eased obtaining of the medicines with different action and allowed to standardize technological process documenting according to GMP requirements.

Ant Colony Optimization for Mapping, Scheduling and Placing in Reconfigurable Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ferrandi, Fabrizio; Lanzi, Pier Luca; Pilato, Christian

Modern heterogeneous embedded platforms, com- posed of several digital signal, application specific and general purpose processors, also include reconfigurable devices support- ing partial dynamic reconfiguration. These devices can change the behavior of some of their parts during execution, allowing hardware acceleration of more sections of the applications. Never- theless, partial dynamic reconfiguration imposes severe overheads in terms of latency. For such systems, a critical part of the design phase is deciding on which processing elements (mapping) and when (scheduling) executing a task, but also how to place them on the reconfigurable device to guarantee the most efficient reuse of themore » programmable logic. In this paper we propose an algorithm based on Ant Colony Optimization (ACO) that simultaneously executes the scheduling, the mapping and the linear placing of tasks, hiding reconfiguration overheads through prefetching. Our heuristic gradually constructs solutions and then searches around the best ones, cutting out non-promising areas of the design space. We show how to consider the partial dynamic reconfiguration constraints in the scheduling, placing and mapping problems and compare our formulation to other heuristics that address the same problems. We demonstrate that our proposal is more general and robust, and finds better solutions (16.5% in average) with respect to competing solutions.« less

Inverse estimation of the spheroidal particle size distribution using Ant Colony Optimization algorithms in multispectral extinction technique

NASA Astrophysics Data System (ADS)

He, Zhenzong; Qi, Hong; Wang, Yuqing; Ruan, Liming

2014-10-01

Four improved Ant Colony Optimization (ACO) algorithms, i.e. the probability density function based ACO (PDF-ACO) algorithm, the Region ACO (RACO) algorithm, Stochastic ACO (SACO) algorithm and Homogeneous ACO (HACO) algorithm, are employed to estimate the particle size distribution (PSD) of the spheroidal particles. The direct problems are solved by the extended Anomalous Diffraction Approximation (ADA) and the Lambert-Beer law. Three commonly used monomodal distribution functions i.e. the Rosin-Rammer (R-R) distribution function, the normal (N-N) distribution function, and the logarithmic normal (L-N) distribution function are estimated under dependent model. The influence of random measurement errors on the inverse results is also investigated. All the results reveal that the PDF-ACO algorithm is more accurate than the other three ACO algorithms and can be used as an effective technique to investigate the PSD of the spheroidal particles. Furthermore, the Johnson's SB (J-SB) function and the modified beta (M-β) function are employed as the general distribution functions to retrieve the PSD of spheroidal particles using PDF-ACO algorithm. The investigation shows a reasonable agreement between the original distribution function and the general distribution function when only considering the variety of the length of the rotational semi-axis.

Filter-feeding, near-field flows, and the morphologies of colonial choanoflagellates

NASA Astrophysics Data System (ADS)

Kirkegaard, Julius B.; Goldstein, Raymond E.

2016-11-01

Efficient uptake of prey and nutrients from the environment is an important component in the fitness of all microorganisms, and its dependence on size may reveal clues to the origins of evolutionary transitions to multicellularity. Because potential benefits in uptake rates must be viewed in the context of other costs and benefits of size, such as varying predation rates and the increased metabolic costs associated with larger and more complex body plans, the uptake rate itself is not necessarily that which is optimized by evolution. Uptake rates can be strongly dependent on local organism geometry and its swimming speed, providing selective pressure for particular arrangements. Here we examine these issues for choanoflagellates, filter-feeding microorganisms that are the closest relatives of the animals. We explore the different morphological variations of the choanoflagellate Salpingoeca rosetta, which can exist as a swimming cell, as a sessile thecate cell, and as colonies of cells in various shapes. In the absence of other requirements and in a homogeneously nutritious environment, we find that the optimal strategy to maximize filter-feeding by the collar of microvilli is to swim fast, which favors swimming unicells. In large external flows, the sessile thecate cell becomes advantageous. Effects of prey diffusion are discussed and also found to be to the advantage of the swimming unicell.

Evolutionary algorithms for the optimal management of coastal groundwater: A comparative study toward future challenges

NASA Astrophysics Data System (ADS)

Ketabchi, Hamed; Ataie-Ashtiani, Behzad

2015-01-01

This paper surveys the literature associated with the application of evolutionary algorithms (EAs) in coastal groundwater management problems (CGMPs). This review demonstrates that previous studies were mostly relied on the application of limited and particular EAs, mainly genetic algorithm (GA) and its variants, to a number of specific problems. The exclusive investigation of these problems is often not the representation of the variety of feasible processes may be occurred in coastal aquifers. In this study, eight EAs are evaluated for CGMPs. The considered EAs are: GA, continuous ant colony optimization (CACO), particle swarm optimization (PSO), differential evolution (DE), artificial bee colony optimization (ABC), harmony search (HS), shuffled complex evolution (SCE), and simplex simulated annealing (SIMPSA). The first application of PSO, ABC, HS, and SCE in CGMPs is reported here. Moreover, the four benchmark problems with different degree of difficulty and variety are considered to address the important issues of groundwater resources in coastal regions. Hence, the wide ranges of popular objective functions and constraints with the number of decision variables ranging from 4 to 15 are included. These benchmark problems are applied in the combined simulation-optimization model to examine the optimization scenarios. Some preliminary experiments are performed to select the most efficient parameters values for EAs to set a fair comparison. The specific capabilities of each EA toward CGMPs in terms of results quality and required computational time are compared. The evaluation of the results highlights EA's applicability in CGMPs, besides the remarkable strengths and weaknesses of them. The comparisons show that SCE, CACO, and PSO yield superior solutions among the EAs according to the quality of solutions whereas ABC presents the poor performance. CACO provides the better solutions (up to 17%) than the worst EA (ABC) for the problem with the highest decision variables and more complexity. In terms of computational time, PSO and SIMPSA are the fastest. SCE needs the highest computational time, even up to four times in comparison to the fastest EAs. CACO and PSO can be recommended for application in CGMPs, in terms of both abovementioned criteria.

Immune defense in leaf-cutting ants: a cross-fostering approach.

PubMed

Armitage, Sophie A O; Broch, Jens F; Marín, Hermogenes Fernández; Nash, David R; Boomsma, Jacobus J

2011-06-01

To ameliorate the impact of disease, social insects combine individual innate immune defenses with collective social defenses. This implies that there are different levels of selection acting on investment in immunity, each with their own trade-offs. We present the results of a cross-fostering experiment designed to address the influences of genotype and social rearing environment upon individual and social immune defenses. We used a multiply mating leaf-cutting ant, enabling us to test for patriline effects within a colony, as well as cross-colony matriline effects. The worker's father influenced both individual innate immunity (constitutive antibacterial activity) and the size of the metapleural gland, which secretes antimicrobial compounds and functions in individual and social defense, indicating multiple mating could have important consequences for both defense types. However, the primarily social defense, a Pseudonocardia bacteria that helps to control pathogens in the ants' fungus garden, showed a significant colony of origin by rearing environment interaction, whereby ants that acquired the bacteria of a foster colony obtained a less abundant cover of bacteria: one explanation for this pattern would be co-adaptation between host colonies and their vertically transmitted mutualist. These results illustrate the complexity of the selection pressures that affect the expression of multilevel immune defenses. © 2011 The Author(s). Evolution© 2011 The Society for the Study of Evolution.

Colony-level assessment of Brucella and Leptospira in the Guadalupe fur seal, Isla Guadalupe, Mexico.

PubMed

Ziehl-Quirós, E Carolina; García-Aguilar, María C; Mellink, Eric

2017-01-24

The relatively small population size and restricted distribution of the Guadalupe fur seal Arctocephalus townsendi could make it highly vulnerable to infectious diseases. We performed a colony-level assessment in this species of the prevalence and presence of Brucella spp. and Leptospira spp., pathogenic bacteria that have been reported in several pinniped species worldwide. Forty-six serum samples were collected in 2014 from pups at Isla Guadalupe, the only place where the species effectively reproduces. Samples were tested for Brucella using 3 consecutive serological tests, and for Leptospira using the microscopic agglutination test. For each bacterium, a Bayesian approach was used to estimate prevalence to exposure, and an epidemiological model was used to test the null hypothesis that the bacterium was present in the colony. No serum sample tested positive for Brucella, and the statistical analyses concluded that the colony was bacterium-free with a 96.3% confidence level. However, a Brucella surveillance program would be highly recommendable. Twelve samples were positive (titers 1:50) to 1 or more serovars of Leptospira. The prevalence was calculated at 27.1% (95% credible interval: 15.6-40.3%), and the posterior analyses indicated that the colony was not Leptospira-free with a 100% confidence level. Serovars Icterohaemorrhagiae, Canicola, and Bratislava were detected, but only further research can unveil whether they affect the fur seal population.

Evolving African attitudes to European education: Resistance, pervert effects of the single system paradox, and the ubuntu framework for renewal

NASA Astrophysics Data System (ADS)

Assié-Lumumba, N'Dri Thérèse

2016-02-01

This paper is a reflection that critically examines the dynamics of education and the struggle by African people for freedom, control of the mind, self-definition and the right to determine their own destiny from the start of colonial rule to the present. The primary methodological approach is historical structuralism, which stipulates that social reality and facts are determined and created by social agents within structural and historical contingencies. It addresses some of the most powerful challenges and contradictions that explain the ineffectiveness of numerous post-independence reforms, and presents the arguments for relevance and use of African languages, for instance, that have been made since the 1960s. The first section of the paper deals with the colonial imperatives for setting new education systems in the colonised societies of Africa and the initial attitudes of the Africans towards colonial education. The second section critically examines the evolving meanings of Western education in Europeanising African societies, the articulation of their rationale and the mechanism for resistance. It analyses the turning point when Africans began to embrace European education and demand it in the colonial and post-independence era. The third section addresses the roots of the inadequacies of received post-colonial education and the imperative of deconstruction and re-appropriation of African education using an ubuntu framework for an African renewal.

Laboratory Animal Management Assistant (LAMA): a LIMS for active research colonies.

PubMed

Milisavljevic, Marko; Hearty, Taryn; Wong, Tony Y T; Portales-Casamar, Elodie; Simpson, Elizabeth M; Wasserman, Wyeth W

2010-06-01

Laboratory Animal Management Assistant (LAMA) is an internet-based system for tracking large laboratory mouse colonies. It has a user-friendly interface with powerful search capabilities that ease day-to-day tasks such as tracking breeding cages and weaning litters. LAMA was originally developed to manage hundreds of new mouse strains generated by a large functional genomics program, the Pleiades Promoter Project ( http://www.pleiades.org ). The software system has proven to be highly flexible, suitable for diverse management approaches to mouse colonies. It allows custom tagging and grouping of animals, simplifying project-specific handling and access to data. Finally, LAMA was developed in close collaboration with mouse technicians to ease the transition from paper- or Excel-based management systems to computerized tracking, allowing data export in a popular spreadsheet format and automatic printing of cage cards. LAMA is an open-access software tool, freely available to the research community at http://launchpad.net/mousedb .

Vitrification of mouse embryo-derived ICM cells: a tool for preserving embryonic stem cell potential?

PubMed

Desai, Nina; Xu, Jing; Tsulaia, Tamara; Szeptycki-Lawson, Julia; AbdelHafez, Faten; Goldfarb, James; Falcone, Tommaso

2011-02-01

Vitrification technology presents new opportunities for preservation of embryo derived stem cells without first establishing a viable ESC line. This study tests the feasibility of cryopreserving ICM cells using vitrification. ICMs from mouse embryos were isolated and vitrified in HSV straws or on cryoloops. Upon warming, the vitrified ICMs were cultured and observed for attachment and morphology. Colonies were passaged every 3-6 days. ICMs and ICM-derived ESC colonies were tested for expression of stem cell specific markers. ICMs vitrified on both the cryoloop and the HSV straw had high survival rates. ICM derived ESCs remained undifferentiated for several passages and demonstrated expression of typical stem cell markers; SSEA-1, Sox-2, Oct 4 and alkaline phosphatase. This is the first report on successful vitrification of isolated ICMs and the subsequent derivation of ESC colonies. Vitrification of isolated ICMs is a novel approach for preservation of the "stem cell source" material.

Colonised and neurasthenic: from the appropriation of a word to the reality of a malaise de civilisation in urban French Vietnam.

PubMed

Monnais, Laurence

2012-01-01

Neurasthenia remains an important health problem in certain Asian populations, both in Asia as well as in a diasporic context. An anachronistic disease for Western observers, it has become an exotic culture-bound syndrome as well as a somatoform disorder too often hiding much more serious issues of depression. This article approaches this 'problematic' health issue from a historian's point of view and offers a colonial genealogy that will discuss neurasthenia's outline in French Vietnam. By retracing and analysing the different mentions, definitions, and uses of the term neurasthenia in the interwar period, it aims to better understand certain historical realities that might have shaped the local identity and spatiality of this problem (concentrated in colonial cities in which social change and modernity were expressed in their most salient forms), and perhaps even identify reasons that facilitated its post-colonial survival.

Honey Bees Inspired Optimization Method: The Bees Algorithm.

PubMed

Yuce, Baris; Packianather, Michael S; Mastrocinque, Ernesto; Pham, Duc Truong; Lambiase, Alfredo

2013-11-06

Optimization algorithms are search methods where the goal is to find an optimal solution to a problem, in order to satisfy one or more objective functions, possibly subject to a set of constraints. Studies of social animals and social insects have resulted in a number of computational models of swarm intelligence. Within these swarms their collective behavior is usually very complex. The collective behavior of a swarm of social organisms emerges from the behaviors of the individuals of that swarm. Researchers have developed computational optimization methods based on biology such as Genetic Algorithms, Particle Swarm Optimization, and Ant Colony. The aim of this paper is to describe an optimization algorithm called the Bees Algorithm, inspired from the natural foraging behavior of honey bees, to find the optimal solution. The algorithm performs both an exploitative neighborhood search combined with random explorative search. In this paper, after an explanation of the natural foraging behavior of honey bees, the basic Bees Algorithm and its improved versions are described and are implemented in order to optimize several benchmark functions, and the results are compared with those obtained with different optimization algorithms. The results show that the Bees Algorithm offering some advantage over other optimization methods according to the nature of the problem.

antaRNA: ant colony-based RNA sequence design.

PubMed

Kleinkauf, Robert; Mann, Martin; Backofen, Rolf

2015-10-01

RNA sequence design is studied at least as long as the classical folding problem. Although for the latter the functional fold of an RNA molecule is to be found ,: inverse folding tries to identify RNA sequences that fold into a function-specific target structure. In combination with RNA-based biotechnology and synthetic biology ,: reliable RNA sequence design becomes a crucial step to generate novel biochemical components. In this article ,: the computational tool antaRNA is presented. It is capable of compiling RNA sequences for a given structure that comply in addition with an adjustable full range objective GC-content distribution ,: specific sequence constraints and additional fuzzy structure constraints. antaRNA applies ant colony optimization meta-heuristics and its superior performance is shown on a biological datasets. http://www.bioinf.uni-freiburg.de/Software/antaRNA CONTACT: backofen@informatik.uni-freiburg.de Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press.

A Multistrategy Optimization Improved Artificial Bee Colony Algorithm

PubMed Central

Liu, Wen

2014-01-01

Being prone to the shortcomings of premature and slow convergence rate of artificial bee colony algorithm, an improved algorithm was proposed. Chaotic reverse learning strategies were used to initialize swarm in order to improve the global search ability of the algorithm and keep the diversity of the algorithm; the similarity degree of individuals of the population was used to characterize the diversity of population; population diversity measure was set as an indicator to dynamically and adaptively adjust the nectar position; the premature and local convergence were avoided effectively; dual population search mechanism was introduced to the search stage of algorithm; the parallel search of dual population considerably improved the convergence rate. Through simulation experiments of 10 standard testing functions and compared with other algorithms, the results showed that the improved algorithm had faster convergence rate and the capacity of jumping out of local optimum faster. PMID:24982924

Hybrid Pareto artificial bee colony algorithm for multi-objective single machine group scheduling problem with sequence-dependent setup times and learning effects.

PubMed

Yue, Lei; Guan, Zailin; Saif, Ullah; Zhang, Fei; Wang, Hao

2016-01-01

Group scheduling is significant for efficient and cost effective production system. However, there exist setup times between the groups, which require to decrease it by sequencing groups in an efficient way. Current research is focused on a sequence dependent group scheduling problem with an aim to minimize the makespan in addition to minimize the total weighted tardiness simultaneously. In most of the production scheduling problems, the processing time of jobs is assumed as fixed. However, the actual processing time of jobs may be reduced due to "learning effect". The integration of sequence dependent group scheduling problem with learning effects has been rarely considered in literature. Therefore, current research considers a single machine group scheduling problem with sequence dependent setup times and learning effects simultaneously. A novel hybrid Pareto artificial bee colony algorithm (HPABC) with some steps of genetic algorithm is proposed for current problem to get Pareto solutions. Furthermore, five different sizes of test problems (small, small medium, medium, large medium, large) are tested using proposed HPABC. Taguchi method is used to tune the effective parameters of the proposed HPABC for each problem category. The performance of HPABC is compared with three famous multi objective optimization algorithms, improved strength Pareto evolutionary algorithm (SPEA2), non-dominated sorting genetic algorithm II (NSGAII) and particle swarm optimization algorithm (PSO). Results indicate that HPABC outperforms SPEA2, NSGAII and PSO and gives better Pareto optimal solutions in terms of diversity and quality for almost all the instances of the different sizes of problems.

Organization model for Mobile Wireless Sensor Networks inspired in Artificial Bee Colony

NASA Astrophysics Data System (ADS)

Freire Roberto, Guilherme; Castilho Maschi, Luis Fernando; Pigatto, Daniel Fernando; Jaquie Castelo Branco, Kalinka Regina Lucas; Alves Neves, Leandro; Montez, Carlos; Sandro Roschildt Pinto, Alex

2015-01-01

The purpose of this study is to find a self-organizing model for MWSN based on bee colonies in order to reduce the number of messages transmitted among nodes, and thus reduce the overall consumption energy while maintaining the efficiency of message delivery. The results obtained in this article are originated from simulations carried out with SINALGO software, which demonstrates the effectiveness of the proposed approach. The BeeAODV (Bee Ad-Hoc On Demand Distance Vector) proposed in this paper allows to considerably reduce message exchanges whether compared to AODV (Ad-Hoc On Demand Distance Vector).

Vocational Rehabilitation in Zimbabwe: A Socio-Historical Analysis.

ERIC Educational Resources Information Center

Devlieger, Patrick J.

1998-01-01

Addresses the legacy of Zimbabwe's missionary and colonial history; the postcolonial period; approaches to employment of people with disabilities; the impact of migration; and new developments throughout Africa and their implications for vocational rehabilitation. (SK)

Modeling the Population Dynamics of Antibiotic-Resistant Bacteria:. AN Agent-Based Approach

NASA Astrophysics Data System (ADS)

Murphy, James T.; Walshe, Ray; Devocelle, Marc

The response of bacterial populations to antibiotic treatment is often a function of a diverse range of interacting factors. In order to develop strategies to minimize the spread of antibiotic resistance in pathogenic bacteria, a sound theoretical understanding of the systems of interactions taking place within a colony must be developed. The agent-based approach to modeling bacterial populations is a useful tool for relating data obtained at the molecular and cellular level with the overall population dynamics. Here we demonstrate an agent-based model, called Micro-Gen, which has been developed to simulate the growth and development of bacterial colonies in culture. The model also incorporates biochemical rules and parameters describing the kinetic interactions of bacterial cells with antibiotic molecules. Simulations were carried out to replicate the development of methicillin-resistant S. aureus (MRSA) colonies growing in the presence of antibiotics. The model was explored to see how the properties of the system emerge from the interactions of the individual bacterial agents in order to achieve a better mechanistic understanding of the population dynamics taking place. Micro-Gen provides a good theoretical framework for investigating the effects of local environmental conditions and cellular properties on the response of bacterial populations to antibiotic exposure in the context of a simulated environment.

GPS tracking for mapping seabird mortality induced by light pollution.

PubMed

Rodríguez, Airam; Rodríguez, Beneharo; Negro, Juan J

2015-06-02

Light pollution and its consequences on ecosystems are increasing worldwide. Knowledge on the threshold levels of light pollution at which significant ecological impacts emerge and the size of dark refuges to maintain natural nocturnal processes is crucial to mitigate its negative consequences. Seabird fledglings are attracted by artificial lights when they leave their nest at night, causing high mortality. We used GPS data-loggers to track the flights of Cory's shearwater Calonectris diomedea fledglings from nest-burrows to ground, and to evaluate the light pollution levels of overflown areas on Tenerife, Canary Islands, using nocturnal, high-resolution satellite imagery. Birds were grounded at locations closer than 16 km from colonies in their maiden flights, and 50% were rescued within a 3 km radius from the nest-site. Most birds left the nests in the first three hours after sunset. Rescue locations showed radiance values greater than colonies, and flight distance was positively related to light pollution levels. Breeding habitat alteration by light pollution was more severe for inland colonies. We provide scientific-based information to manage dark refuges facilitating that fledglings from inland colonies reach the sea successfully. We also offer methodological approaches useful for other critically threatened petrel species grounded by light pollution.

Differential adult survival at close seabird colonies: The importance of spatial foraging segregation and bycatch risk during the breeding season.

PubMed

Genovart, Meritxell; Bécares, Juan; Igual, José-Manuel; Martínez-Abraín, Alejandro; Escandell, Raul; Sánchez, Antonio; Rodríguez, Beneharo; Arcos, José M; Oro, Daniel

2018-03-01

Marine megafauna, including seabirds, are critically affected by fisheries bycatch. However, bycatch risk may differ on temporal and spatial scales due to the uneven distribution and effort of fleets operating different fishing gear, and to focal species distribution and foraging behavior. Scopoli's shearwater Calonectris diomedea is a long-lived seabird that experiences high bycatch rates in longline fisheries and strong population-level impacts due to this type of anthropogenic mortality. Analyzing a long-term dataset on individual monitoring, we compared adult survival (by means of multi-event capture-recapture models) among three close predator-free Mediterranean colonies of the species. Unexpectedly for a long-lived organism, adult survival varied among colonies. We explored potential causes of this differential survival by (1) measuring egg volume as a proxy of food availability and parental condition; (2) building a specific longline bycatch risk map for the species; and (3) assessing the distribution patterns of breeding birds from the three study colonies via GPS tracking. Egg volume was very similar between colonies over time, suggesting that environmental variability related to habitat foraging suitability was not the main cause of differential survival. On the other hand, differences in foraging movements among individuals from the three colonies expose them to differential mortality risk, which likely influenced the observed differences in adult survival. The overlap of information obtained by the generation of specific bycatch risk maps, the quantification of population demographic parameters, and the foraging spatial analysis should inform managers about differential sensitivity to the anthropogenic impact at mesoscale level and guide decisions depending on the spatial configuration of local populations. The approach would apply and should be considered in any species where foraging distribution is colony-specific and mortality risk varies spatially. © 2017 John Wiley & Sons Ltd.

Modeling colony site dynamics: A case study of gull-billed terns (Sterna nilotica) in coastal Virginia

USGS Publications Warehouse

Erwin, R.M.; Nichols, J.D.; Eyler, T.B.; Stotts, D.B.; Truitt, B.R.

1998-01-01

We developed a Markov process model for colony-site dynamics of Gull-billed Terns (Sterna nilotica). From 1993 through 1996, we monitored breeding numbers of Gull-billed Terns and their frequent colony associates, Common Terns (Sterna hirundo) and Black Skimmers (Rynchops niger), at colony sites along 80 km of the barrier island region of coastal Virginia. We also monitored flooding events and renesting. We developed the model for colony survival, extinction, and recolonization at potential colony sites over the four-year period. We then used data on annual site occupation by Gull-billed Terns to estimate model parameters and tested for differences between nesting substrates (barrier island vs. shellpile). Results revealed a dynamic system but provided no evidence that the dynamics were Markovian, i.e. the probability that a site was occupied in one year was not influenced by whether it had been occupied in the previous year. Nor did colony-level reproductive success the previous season seem to affect the probability of site occupancy. Site survival and recolonization rates were similar, and the estimated overall annual probability of a site being occupied was 0.59. Of the 25 sites that were used during the four-year period, 16 were used in one or two years only, and only three were used in all four years. Flooding and renesting were frequent in both habitat types in all years. The frequent flooding of nests on shellpiles argues for more effective management; augmentation with shell and sand to increase elevations as little as 20 cm could have reduced flooding at a number of sites. The low colonysite fidelity that we observed suggests that an effective management approach would be to provide a large number of sand and/or shellpile sites for use by nesting terns. Sites not used in one year may still be used in subsequent years.

Growth Dynamics of the Threatened Caribbean Staghorn Coral Acropora cervicornis: Influence of Host Genotype, Symbiont Identity, Colony Size, and Environmental Setting

PubMed Central

Lirman, Diego; Schopmeyer, Stephanie; Galvan, Victor; Drury, Crawford; Baker, Andrew C.; Baums, Iliana B.

2014-01-01

Background The drastic decline in the abundance of Caribbean acroporid corals (Acropora cervicornis, A. palmata) has prompted the listing of this genus as threatened as well as the development of a regional propagation and restoration program. Using in situ underwater nurseries, we documented the influence of coral genotype and symbiont identity, colony size, and propagation method on the growth and branching patterns of staghorn corals in Florida and the Dominican Republic. Methodology/Principal Findings Individual tracking of> 1700 nursery-grown staghorn fragments and colonies from 37 distinct genotypes (identified using microsatellites) in Florida and the Dominican Republic revealed a significant positive relationship between size and growth, but a decreasing rate of productivity with increasing size. Pruning vigor (enhanced growth after fragmentation) was documented even in colonies that lost 95% of their coral tissue/skeleton, indicating that high productivity can be maintained within nurseries by sequentially fragmenting corals. A significant effect of coral genotype was documented for corals grown in a common-garden setting, with fast-growing genotypes growing up to an order of magnitude faster than slow-growing genotypes. Algal-symbiont identity established using qPCR techniques showed that clade A (likely Symbiodinium A3) was the dominant symbiont type for all coral genotypes, except for one coral genotype in the DR and two in Florida that were dominated by clade C, with A- and C-dominated genotypes having similar growth rates. Conclusion/Significance The threatened Caribbean staghorn coral is capable of extremely fast growth, with annual productivity rates exceeding 5 cm of new coral produced for every cm of existing coral. This species benefits from high fragment survivorship coupled by the pruning vigor experienced by the parent colonies after fragmentation. These life-history characteristics make A. cervicornis a successful candidate nursery species and provide optimism for the potential role that active propagation can play in the recovery of this keystone species. PMID:25268812

A Chaotic Particle Swarm Optimization-Based Heuristic for Market-Oriented Task-Level Scheduling in Cloud Workflow Systems.

PubMed

Li, Xuejun; Xu, Jia; Yang, Yun

2015-01-01

Cloud workflow system is a kind of platform service based on cloud computing. It facilitates the automation of workflow applications. Between cloud workflow system and its counterparts, market-oriented business model is one of the most prominent factors. The optimization of task-level scheduling in cloud workflow system is a hot topic. As the scheduling is a NP problem, Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) have been proposed to optimize the cost. However, they have the characteristic of premature convergence in optimization process and therefore cannot effectively reduce the cost. To solve these problems, Chaotic Particle Swarm Optimization (CPSO) algorithm with chaotic sequence and adaptive inertia weight factor is applied to present the task-level scheduling. Chaotic sequence with high randomness improves the diversity of solutions, and its regularity assures a good global convergence. Adaptive inertia weight factor depends on the estimate value of cost. It makes the scheduling avoid premature convergence by properly balancing between global and local exploration. The experimental simulation shows that the cost obtained by our scheduling is always lower than the other two representative counterparts.

A Chaotic Particle Swarm Optimization-Based Heuristic for Market-Oriented Task-Level Scheduling in Cloud Workflow Systems

PubMed Central

Li, Xuejun; Xu, Jia; Yang, Yun

2015-01-01

Cloud workflow system is a kind of platform service based on cloud computing. It facilitates the automation of workflow applications. Between cloud workflow system and its counterparts, market-oriented business model is one of the most prominent factors. The optimization of task-level scheduling in cloud workflow system is a hot topic. As the scheduling is a NP problem, Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) have been proposed to optimize the cost. However, they have the characteristic of premature convergence in optimization process and therefore cannot effectively reduce the cost. To solve these problems, Chaotic Particle Swarm Optimization (CPSO) algorithm with chaotic sequence and adaptive inertia weight factor is applied to present the task-level scheduling. Chaotic sequence with high randomness improves the diversity of solutions, and its regularity assures a good global convergence. Adaptive inertia weight factor depends on the estimate value of cost. It makes the scheduling avoid premature convergence by properly balancing between global and local exploration. The experimental simulation shows that the cost obtained by our scheduling is always lower than the other two representative counterparts. PMID:26357510

Population dynamics of Hawaiian seabird colonies vulnerable to sea-level rise

USGS Publications Warehouse

Hatfield, Jeff S.; Reynolds, Michelle H.; Seavy, Nathaniel E.; Krause, Crystal M.

2012-01-01

Globally, seabirds are vulnerable to anthropogenic threats both at sea and on land. Seabirds typically nest colonially and show strong fidelity to natal colonies, and such colonies on low-lying islands may be threatened by sea-level rise. We used French Frigate Shoals, the largest atoll in the Hawaiian Archipelago, as a case study to explore the population dynamics of seabird colonies and the potential effects sea-level rise may have on these rookeries. We compiled historic observations, a 30-year time series of seabird population abundance, lidar-derived elevations, and aerial imagery of all the islands of French Frigate Shoals. To estimate the population dynamics of 8 species of breeding seabirds on Tern Island from 1980 to 2009, we used a Gompertz model with a Bayesian approach to infer population growth rates, density dependence, process variation, and observation error. All species increased in abundance, in a pattern that provided evidence of density dependence. Great Frigatebirds (Fregata minor), Masked Boobies (Sula dactylatra), Red-tailed Tropicbirds (Phaethon rubricauda), Spectacled Terns (Onychoprion lunatus), and White Terns (Gygis alba) are likely at carrying capacity. Density dependence may exacerbate the effects of sea-level rise on seabirds because populations near carrying capacity on an island will be more negatively affected than populations with room for growth. We projected 12% of French Frigate Shoals will be inundated if sea level rises 1 m and 28% if sea level rises 2 m. Spectacled Terns and shrub-nesting species are especially vulnerable to sea-level rise, but seawalls and habitat restoration may mitigate the effects of sea-level rise. Losses of seabird nesting habitat may be substantial in the Hawaiian Islands by 2100 if sea levels rise 2 m. Restoration of higher-elevation seabird colonies represent a more enduring conservation solution for Pacific seabirds.

Survival ability of Mexican fruit fly males from different strains in presence of the predatory orb-weaving spider Argiope argentata (Araneae: Araneidae).

PubMed

Dor, A; Liedo, P

2018-04-18

The sterile insect technique (SIT) is a key element for the integrated management of pest populations of the Mexican fruit fly, Anastrepha ludens, in Mexico. Its success depends on the survival of mass-reared sterile males and their ability to mate with wild females. However, colonization and mass-rearing conditions can adversely affect their ability to avoid predators. To test if colony management strategies could contribute to improve field survival abilities of mass-reared flies, we compared the survival of males exposed to the orb-weaver spider Argiope argentata. Males compared originated from three strains with different colonization strategies: (a) a colony started from field-collected wild flies (replacement), (b) a colony started by hybridizing wild males with mass-reared adapted females (hybrid) and (c) a colony started with mass-reared males selected on the basis of their survival ability and mating competitiveness in field cages (selected). Mass-reared males and wild males were used as controls. Males were exposed to spiders under laboratory cage conditions. Overall, wild males showed better survival ability than mass-reared males. Regarding the colonization approach, wild males survived better than a hybrid, replaced and selected males. We conclude that mass-rearing conditions have a strong negative effect on the ability of males to escape spiders. The colonization systems evaluated did not counter this effect. The lower survival of males from the selected colony suggests that the selection over one generation did not contribute to improve males' predator avoidance and escape abilities and probably needs to be modified. Possible explanations for this and implications on colonization and colony management for SIT purpose are discussed.

The global distribution of ammonia emissions from seabird colonies

NASA Astrophysics Data System (ADS)

Riddick, S. N.; Dragosits, U.; Blackall, T. D.; Daunt, F.; Wanless, S.; Sutton, M. A.

2012-08-01

Seabird colonies represent a significant source of atmospheric ammonia (NH3) in remote maritime systems, producing a source of nitrogen that may encourage plant growth, alter terrestrial plant community composition and affect the surrounding marine ecosystem. To investigate seabird NH3 emissions on a global scale, we developed a contemporary seabird database including a total seabird population of 261 million breeding pairs. We used this in conjunction with a bioenergetics model to estimate the mass of nitrogen excreted by all seabirds at each breeding colony. The results combined with the findings of mid-latitude field studies of volatilization rates estimate the global distribution of NH3 emissions from seabird colonies on an annual basis. The largest uncertainty in our emission estimate concerns the potential temperature dependence of NH3 emission. To investigate this we calculated and compared temperature independent emission estimates with a maximum feasible temperature dependent emission, based on the thermodynamic dissociation and solubility equilibria. Using the temperature independent approach, we estimate global NH3 emissions from seabird colonies at 404 Gg NH3 per year. By comparison, since most seabirds are located in relatively cold circumpolar locations, the thermodynamically dependent estimate is 136 Gg NH3 per year. Actual global emissions are expected to be within these bounds, as other factors, such as non-linear interactions with water availability and surface infiltration, moderate the theoretical temperature response. Combining sources of error from temperature (±49%), seabird population estimates (±36%), variation in diet composition (±23%) and non-breeder attendance (±13%), gives a mid estimate with an overall uncertainty range of NH3 emission from seabird colonies of 270 [97-442] Gg NH3 per year. These emissions are environmentally relevant as they primarily occur as "hot-spots" in otherwise pristine environments with low anthropogenic emissions.

Microengineered embryonic stem cells niche to induce neural differentiation.

PubMed

Joshi, Ramila; Tavana, Hossein

2015-08-01

A major challenge in therapeutic use of embryonic stem cells (ESCs) for treating neurodegenerative diseases is creating a niche in vitro for controlled neural-specific differentiation of ESCs. We employ a niche microengineering approach to derive neural cells from ESCs by mimicking embryonic development in terms of direct intercellular interactions. Using a polymeric aqueous two-phase system (ATPS) microprinting technology, murine ESCs (mESCs) are precisely localized over a monolayer of supporting stromal cells to allow formation of individual mESC colonies. Polyethylene glycol (PEG) and dextran (DEX) are dissolved in culture media to form two immiscible aqueous solutions. A robotic liquid handler is used to print a nanoliter-volume drop of the denser DEX phase solution containing mESCs onto a confluent layer of supporting PA6 stromal cells submerged in the aqueous PEG phase. mESCs proliferate into isolated colonies of uniform size. For the first time, a comprehensive protein expression analysis of individual mESC colonies is performed over a two-week culture period to track temporal progression of cells from a pluripotent stage to specific neural cells. Starting from day 4, the expression of nestin, neural cell adhesion molecule (NCAM), and beta-III tubulin shows a significant increase but then levels off after the first week of culture. The expression of specific neural cell markers glial fibrillary acidic protein (GFAP), 2',3'-cyclic-nucleotide 3'-phosphodiesterase (CNPase), and tyrosine hydroxylase (TH) is elevated during the second week of culture. This microengineering approach to control ESCs differentiation niche combined with the time-course protein expression analysis of individual differentiating colonies facilitates understanding of evolution of specific neural cells from ESCs and identifying underlying molecular markers.

[The post-colonial theoretical approach in cultural nursing research about adapting nursing care to non-Western populations].

PubMed

Racine, Louise

2003-12-01

Providing culturally competent care to non-Western populations remains a challenge in Western pluralist societies. Limitations related to the use of cultural theories to adapt nursing care have to be acknowledged. Cultural theories erase the impact of the larger social context on non-Western populations' health. Health problems arising from social inequities have to be addressed, if culturally adapted nursing interventions, are to be designed. To this end, post-colonialist theoretical approach represents a promising avenue since it is aimed at unmasking health problems intersecting with race, ethnicity, gender, and social classes. Research endeavours are directed at integrating marginalized knowledge in nursing theorization. As well, post-colonialism, from which the concept of cultural safety is derived, is a means to enhance the quality of care offered by nurses coming from dominant ethnic group to non-Western populations.

An evaluation of the choice of feeder cell growth arrest for the production of cultured epidermis.

PubMed

Chugh, Rishi Man; Chaturvedi, Madhusudan; Yerneni, Lakshmana Kumar

2015-12-01

Growth arrested 3T3 cells have been used as feeder cells in human epidermal keratinocyte cultures to produce cultured epidermal autografts for the treatment of burns. The feeder cells were ideally growth-arrested by gamma-irradiation. Alternatively, growth arrest by mitomycin C treatment is a cost effective option. We compared the functional efficacy of these two approaches in keratinocyte cultures by colony forming efficiency, the net growth area of colonies, BrdU labeling and histological features of cultured epidermal sheets. The growth area estimation involved a semi-automated digital technique using the Adobe Photoshop and comprised of isolation and enumeration of red pixels in Rhodamine B-stained keratinocyte colonies. A further refinement of the technique led to the identification of critical steps to increasing the degree of accuracy and enabling its application as an extension of colony formation assay. The results on feeder cell functionality revealed that the gamma irradiated feeders influenced significantly higher colony forming efficiency and larger growth area than the mitomycin C treated feeders. The BrdU labeling study indicated significant stimulation of the overall keratinocyte proliferation by the gamma irradiated feeders. The cultured epidermal sheets produced by gamma feeders were relatively thicker than those produced by mitomycin C feeders. We discussed the clinical utility of mitomycin C feeders from the viewpoint of cost-effective burn care in developing countries. Copyright © 2015 Elsevier Ltd and ISBI. All rights reserved.

A Technical Survey on Optimization of Processing Geo Distributed Data

NASA Astrophysics Data System (ADS)

Naga Malleswari, T. Y. J.; Ushasukhanya, S.; Nithyakalyani, A.; Girija, S.

2018-04-01

With growing cloud services and technology, there is growth in some geographically distributed data centers to store large amounts of data. Analysis of geo-distributed data is required in various services for data processing, storage of essential information, etc., processing this geo-distributed data and performing analytics on this data is a challenging task. The distributed data processing is accompanied by issues in storage, computation and communication. The key issues to be dealt with are time efficiency, cost minimization, utility maximization. This paper describes various optimization methods like end-to-end multiphase, G-MR, etc., using the techniques like Map-Reduce, CDS (Community Detection based Scheduling), ROUT, Workload-Aware Scheduling, SAGE, AMP (Ant Colony Optimization) to handle these issues. In this paper various optimization methods and techniques used are analyzed. It has been observed that end-to end multiphase achieves time efficiency; Cost minimization concentrates to achieve Quality of Service, Computation and reduction of Communication cost. SAGE achieves performance improvisation in processing geo-distributed data sets.

Reducing time to identification of positive blood cultures with MALDI-TOF MS analysis after a 5-h subculture.

PubMed

Verroken, A; Defourny, L; Lechgar, L; Magnette, A; Delmée, M; Glupczynski, Y

2015-02-01

Speeding up the turn-around time of positive blood culture identifications is essential in order to optimize the treatment of septic patients. Several sample preparation techniques have been developed allowing direct matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) identification of positive blood cultures. Yet, the hands-on time restrains their routine workflow. In this study, we evaluated an approach whereby MALDI-TOF MS identification without any additional steps was carried out on short subcultured colonies from positive blood bottles with the objective of allowing results reporting on the day of positivity detection. Over a 7-month period in 2012, positive blood cultures detected by 9 am with an automated system were inoculated onto a Columbia blood agar and processed after a 5-h incubation on a MALDI-TOF MicroFlex platform (Bruker Daltonik GmbH). Single-spotted colonies were covered with 1 μl formic acid and 1 μl matrix solution. The results were compared to the validated identification techniques. A total of 925 positive blood culture bottles (representing 470 bacteremic episodes) were included. Concordant identification was obtained in 727 (81.1 %) of the 896 monomicrobial blood cultures, with failure being mostly observed with anaerobes and yeasts. In 17 episodes of polymicrobic bacteremia, the identification of one of the two isolates was achieved in 24/29 (82.7 %) positive cultures. Routine implementation of MALDI-TOF MS identification on young positive blood subcultures provides correct results to the clinician in more than 80 % of the bacteremic episodes and allows access to identification results on the day of blood culture positivity detection, potentially accelerating the implementation of targeted clinical treatments.

Use of the superpopulation approach to estimate breeding population size: An example in asynchronously breeding birds

USGS Publications Warehouse

Williams, K.A.; Frederick, P.C.; Nichols, J.D.

2011-01-01

Many populations of animals are fluid in both space and time, making estimation of numbers difficult. Much attention has been devoted to estimation of bias in detection of animals that are present at the time of survey. However, an equally important problem is estimation of population size when all animals are not present on all survey occasions. Here, we showcase use of the superpopulation approach to capture-recapture modeling for estimating populations where group membership is asynchronous, and where considerable overlap in group membership among sampling occasions may occur. We estimate total population size of long-legged wading bird (Great Egret and White Ibis) breeding colonies from aerial observations of individually identifiable nests at various times in the nesting season. Initiation and termination of nests were analogous to entry and departure from a population. Estimates using the superpopulation approach were 47-382% larger than peak aerial counts of the same colonies. Our results indicate that the use of the superpopulation approach to model nesting asynchrony provides a considerably less biased and more efficient estimate of nesting activity than traditional methods. We suggest that this approach may also be used to derive population estimates in a variety of situations where group membership is fluid. ?? 2011 by the Ecological Society of America.

Development and validation of a FISH-based method for the detection and quantification of E. coli and coliform bacteria in water samples.

PubMed

Hügler, Michael; Böckle, Karin; Eberhagen, Ingrid; Thelen, Karin; Beimfohr, Claudia; Hambsch, Beate

2011-01-01

Monitoring of microbiological contaminants in water supplies requires fast and sensitive methods for the specific detection of indicator organisms or pathogens. We developed a protocol for the simultaneous detection of E. coli and coliform bacteria based on the Fluorescence in situ Hybridization (FISH) technology. This protocol consists of two approaches. The first allows the direct detection of single E. coli and coliform bacterial cells on the filter membranes. The second approach includes incubation of the filter membranes on a nutrient agar plate and subsequent detection of the grown micro-colonies. Both approaches were validated using drinking water samples spiked with pure cultures and naturally contaminated water samples. The effects of heat, chlorine and UV disinfection were also investigated. The micro-colony approach yielded very good results for all samples and conditions tested, and thus can be thoroughly recommended for usage as an alternative method to detect E. coli and coliform bacteria in water samples. However, during this study, some limitations became visible for the single cell approach. The method cannot be applied for water samples which have been disinfected by UV irradiation. In addition, our results indicated that green fluorescent dyes are not suitable to be used with chlorine disinfected samples.