Induction of the split sting trait in Africanized Apis mellifera (Hymenoptera : Apidae) by cold treatment of pupae

Split sting is the name given to a nonfunctional honey bee sting characterized by lancets not attached to the stylet. It has appeared in a mutant line in Brazil, and has provoked interest as a possible means to reduce honey bee colony defensiveness. We induced this alteration in Africanized Apis mellifera L. workers and queens by maintaining pupae at 20 degrees C. In particular, we determined the pupal phase most susceptible to alterations in the sting caused by cold treatment, and we investigated whether this treatment also affected survival to the adult phase and wing morphology. The highest frequency of split sting was detected in workers treated at the pink-eyed pupal phase. The lowest frequency was observed in the bees treated at the oldest worker pupal phase studied (brown-eyed pupae with lightly pigmented cuticle). Both queen pupal phases tested (white and pink-eyed pupae) were equally sensitive and produced high percentages of adults with split sting. However, the 20 degrees C treatment of workers and queens, at the different pupal phases, resulted in high frequencies of adults with deformed wings. Also, fewer workers and queens treated at the earlier pupal stages reached adult emergence. There was also an arrest in developmental time, corresponding to the period of cold treatment.

Contributions to underwater glider path planning

[ES] La Planificación de Rutas o Caminos es un disciplina de Robótica que trata la búsqueda de caminos factibles u óptimos. Para la mayoría de vehículos y entornos, no es un problema trivial y por tanto nos encontramos con un gran diversidad de algoritmos para resolverlo, no sólo en Robótica e Inteligencia Artificial, sino también como parte de la literatura de Optimización, con Métodos Numéricos y Algoritmos Bio-inspirados, como Algoritmos Genéticos y el Algoritmo de la Colonia de Hormigas. El caso particular de escenarios de costes variables es considerablemente difícil de abordar porque el entorno en el que se mueve el vehículo cambia con el tiempo. El presente trabajo de tesis estudia este problema y propone varias soluciones prácticas para aplicaciones de Robótica Submarina.

Ottimizzazione mediante ant colonies del tempo di evacuazione dei passeggeri dagli aerei da trasporto

Sviluppo di un modello di ottimizzazione dei tempi di evacuazione da aerei da trasporto mediante disposizione intelligente dei passeggeri effettuata con un algoritmo basato sulla Ant Colony Optimization.

Sublethal effects of a common neonicotinoid pesticide, thiamethoxam, on honey bees: impact on locomotion and thermoregulation

Neonicotinoids have been pointed to as a factor responsible for the increased honey bee colony losses in the last decades. Many studies have investigated the effects of the first marketed neonicotinoid, imidacloprid, while fewer have focused on thiamethoxam. One recent study showed that sublethal doses of thiamethoxam lead to colony failure by decreasing forager homing flight success. We thus decided to investigate the mechanism which caused this phenomenon. Our hypothesis was that this effect was caused by impairment of forager locomotion abilities. Therefore we tested the effects of sublethal acute and chronic exposures to thiamethoxam on forager walking (Chapter 2) and flight (Chapter 3) performances. The acute treatment (1.34 ng/bee) affected walking locomotion firstly triggering hyperactivity (30 min post-treatment) and then impairing motor functioning (60 min post-treatment). 2-day continuous exposures to thiamethoxam (32.5, 45 ppb) elicited fewer effects on walking locomotion, however both exposure modes elicited an increased positive phototaxis. Similarly, in flight experiments, the single dose (1.34 ng/bee) elicited hyperactivity shortly after intoxication (increased flight duration and distance), while longer and continuous exposures (32.5, 45 ppb) impaired forager motor functions (decreased flight duration, distance, velocity). It is known that flight muscles temperature needs to be precisely regulated by bees during flight. Therefore, we further hypothesized that the impaired flight performances of neonicotinoid intoxicated bees were caused also by thermoregulation anomalies. We tested the effects that acute thiamethoxam exposures (0.2, 1, 2 ng/bee) elicit on forager thorax temperature (Chapter 4). Foragers treated with high doses exhibited hyperthermia or hypothermia when respectively exposed to high or low environmental temperatures. In summary, we show that sublethal doses of thiamethoxam affected forager walking and flight locomotion, phototaxis and thermoregulation. We also display the intricate mode of action of thiamethoxam which triggered, at different extents, inverse sublethal effects in relation to time and dose.

Metaheuristics for the Order Batching Problem in Manual Order Picking Systems

In manual order picking systems, order pickers walk or drive through a distribution warehouse in order to collect items which are requested by (internal or external) customers. In order to perform these operations efficiently, it is usually required that customer orders are combined into (more substantial) picking orders of limited size. The Order Batching Problem considered in this paper deals with the question of how a given set of customer orders should be combined such that the total length of all tours is minimized which are necessary to collect all items. The authors introduce two metaheuristic approaches for the solution of this problem: the first one is based on Iterated Local Search; the second on Ant Colony Optimization. In a series of extensive numerical experiments, the newly developed approaches are benchmarked against classic solution methods. It is demonstrated that the proposed methods are not only superior to existing methods but provide solutions which may allow distribution warehouses to be operated significantly more efficiently.

Metaheuristic approaches for MWT and MWPT problems

It is known that the Minimum Weight Triangulation problem is NP-hard. Also the complexity of the Minimum Weight Pseudo-Triangulation problem is unknown, yet it is suspected to be also NP-hard. Therefore we focused on the development of approximate algorithms to find high quality triangulations and pseudo-triangulations of minimum weight. In this work we propose two metaheuristics to solve these problems: Ant Colony Optimization (ACO) and Simulated Annealing (SA). For the experimental study we have created a set of instances for MWT and MWPT problems, since no reference to benchmarks for these problems were found in the literature. Through experimental evaluation, we assess the applicability of the ACO and SA metaheuristics for MWT and MWPT problems. These results are compared with those obtained from the application of deterministic algorithms for the same problems (Delaunay Triangulation for MWT and a Greedy algorithm respectively for MWT and MWPT).

Using ACO metaheuristic for MWT problem

Globally optimal triangulations are difficult to be found by deterministic methods as, for most type of criteria, no polynomial algorithm is known. In this work, we consider the Minimum Weight Triangulation (MWT) problem of a given set of n points in the plane. This paper shows how the Ant Colony Optimization (ACO) metaheuristic can be used to find high quality triangulations. For the experimental study we have created a set of instances for MWT problem since no reference to benchmarks for these problems were found in the literature. Through the experimental evaluation, we assess the applicability of the ACO metaheuristic for MWT problem.

Application of self-organizing techniques for the distribution of heterogeneous multi-tasks in multi-robot systems

This paper focuses on the general problem of coordinating of multi-robot systems, more specifically, it addresses the self-election of heterogeneous and specialized tasks by autonomous robots. In this regard, it has proposed experimenting with two different techniques based chiefly on selforganization and emergence biologically inspired, by applying response threshold models as well as ant colony optimization. Under this approach it can speak of multi-tasks selection instead of multi-tasks allocation, that means, as the agents or robots select the tasks instead of being assigned a task by a central controller. The key element in these algorithms is the estimation of the stimuli and the adaptive update of the thresholds. This means that each robot performs this estimate locally depending on the load or the number of pending tasks to be performed. It has evaluated the robustness of the algorithms, perturbing the number of pending loads to simulate the robot’s error in estimating the real number of pending tasks and also the dynamic generation of loads through time. The paper ends with a critical discussion of experimental results.

Arquitectura de supercomputación en la nube para la resolución de problemas de optimización combinatoria

La tesis está focalizada en la resolución de problemas de optimización combinatoria, haciendo uso de las opciones tecnológicas actuales que ofrecen las tecnologías de la información y las comunicaciones, y la investigación operativa. Los problemas de optimización combinatoria se resuelven en general mediante programación lineal y metaheurísticas. La aplicación de las técnicas de resolución de los problemas de optimización combinatoria requiere de una elevada carga computacional, y los algoritmos deben diseñarse, por un lado pensando en la efectividad para encontrar buenas soluciones del problema, y por otro lado, pensando en un uso adecuado de los recursos informáticos disponibles. La programación lineal y las metaheurísticas son técnicas de resolución genéricas, que se pueden aplicar a diferentes problemas, partiendo de una base común que se particulariza para cada problema concreto. En el campo del desarrollo de software, los frameworks cumplen esa función de comenzar un proyecto con el trabajo general ya disponible, con la opción de cambiar o extender ese comportamiento base o genérico, para construir el sistema concreto, lo que permite reducir el tiempo de desarrollo, y amplía las posibilidades de éxito del proyecto. En esta tesis se han desarrollado dos frameworks de desarrollo. El framework ILP permite modelar y resolver problemas de programación lineal, de forma independiente al software de resolución de programación lineal que se utilice. El framework LME permite resolver problemas de optimización combinatoria mediante metaheurísticas. Tradicionalmente, las aplicaciones de resolución de problemas de optimización combinatoria son aplicaciones de escritorio que permiten gestionar toda la información de entrada del problema y resuelven el problema en local, con los recursos hardware disponibles. Recientemente ha aparecido un nuevo paradigma de despliegue y uso de aplicaciones que permite compartir recursos informáticos especializados por Internet. Esta nueva forma de uso de recursos informáticos es la computación en la nube, que presenta el modelo de software como servicio (SaaS). En esta tesis se ha construido una plataforma SaaS, para la resolución de problemas de optimización combinatoria, que se despliega sobre arquitecturas compuestas por procesadores multi-núcleo y tarjetas gráficas, y dispone de algoritmos de resolución basados en frameworks de programación lineal y metaheurísticas. Toda la infraestructura es independiente del problema de optimización combinatoria a resolver, y se han desarrollado tres problemas que están totalmente integrados en la plataforma SaaS. Estos problemas se han seleccionado por su importancia práctica. Uno de los problemas tratados en la tesis, es el problema de rutas de vehículos (VRP), que consiste en calcular las rutas de menor coste de una flota de vehículos, que reparte mercancías a todos los clientes. Se ha partido de la versión más clásica del problema y se han hecho estudios en dos direcciones. Por un lado se ha cuantificado el aumento en la velocidad de ejecución de la resolución del problema en tarjetas gráficas. Por otro lado, se ha estudiado el impacto en la velocidad de ejecución y en la calidad de soluciones, en la resolución por la metaheurística de colonias de hormigas (ACO), cuando se introduce la programación lineal para optimizar las rutas individuales de cada vehículo. Este problema se ha desarrollado con los frameworks ILP y LME, y está disponible en la plataforma SaaS. Otro de los problemas tratados en la tesis, es el problema de asignación de flotas (FAP), que consiste en crear las rutas de menor coste para la flota de vehículos de una empresa de transporte de viajeros. Se ha definido un nuevo modelo de problema, que engloba características de problemas presentados en la literatura, y añade nuevas características, lo que permite modelar los requerimientos de las empresas de transporte de viajeros actuales. Este nuevo modelo resuelve de forma integrada el problema de definir los horarios de los trayectos, el problema de asignación del tipo de vehículo, y el problema de crear las rotaciones de los vehículos. Se ha creado un modelo de programación lineal para el problema, y se ha resuelto por programación lineal y por colonias de hormigas (ACO). Este problema se ha desarrollado con los frameworks ILP y LME, y está disponible en la plataforma SaaS. El último problema tratado en la tesis es el problema de planificación táctica de personal (TWFP), que consiste en definir la configuración de una plantilla de trabajadores de menor coste, para cubrir una demanda de carga de trabajo variable. Se ha definido un modelo de problema muy flexible en la definición de contratos, que permite el uso del modelo en diversos sectores productivos. Se ha definido un modelo matemático de programación lineal para representar el problema. Se han definido una serie de casos de uso, que muestran la versatilidad del modelo de problema, y permiten simular el proceso de toma de decisiones de la configuración de una plantilla de trabajadores, cuantificando económicamente cada decisión que se toma. Este problema se ha desarrollado con el framework ILP, y está disponible en la plataforma SaaS. ABSTRACT The thesis is focused on solving combinatorial optimization problems, using current technology options offered by information technology and communications, and operations research. Combinatorial optimization problems are solved in general by linear programming and metaheuristics. The application of these techniques for solving combinatorial optimization problems requires a high computational load, and algorithms are designed, on the one hand thinking to find good solutions to the problem, and on the other hand, thinking about proper use of the available computing resources. Linear programming and metaheuristic are generic resolution techniques, which can be applied to different problems, beginning with a common base that is particularized for each specific problem. In the field of software development, frameworks fulfill this function that allows you to start a project with the overall work already available, with the option to change or extend the behavior or generic basis, to build the concrete system, thus reducing the time development, and expanding the possibilities of success of the project. In this thesis, two development frameworks have been designed and developed. The ILP framework allows to modeling and solving linear programming problems, regardless of the linear programming solver used. The LME framework is designed for solving combinatorial optimization problems using metaheuristics. Traditionally, applications for solving combinatorial optimization problems are desktop applications that allow the user to manage all the information input of the problem and solve the problem locally, using the available hardware resources. Recently, a new deployment paradigm has appeared, that lets to share hardware and software resources by the Internet. This new use of computer resources is cloud computing, which presents the model of software as a service (SaaS). In this thesis, a SaaS platform has been built for solving combinatorial optimization problems, which is deployed on architectures, composed of multi-core processors and graphics cards, and has algorithms based on metaheuristics and linear programming frameworks. The SaaS infrastructure is independent of the combinatorial optimization problem to solve, and three problems are fully integrated into the SaaS platform. These problems have been selected for their practical importance. One of the problems discussed in the thesis, is the vehicle routing problem (VRP), which goal is to calculate the least cost of a fleet of vehicles, which distributes goods to all customers. The VRP has been studied in two directions. On one hand, it has been quantified the increase in execution speed when the problem is solved on graphics cards. On the other hand, it has been studied the impact on execution speed and quality of solutions, when the problem is solved by ant colony optimization (ACO) metaheuristic, and linear programming is introduced to optimize the individual routes of each vehicle. This problem has been developed with the ILP and LME frameworks, and is available in the SaaS platform. Another problem addressed in the thesis, is the fleet assignment problem (FAP), which goal is to create lower cost routes for a fleet of a passenger transport company. It has been defined a new model of problem, which includes features of problems presented in the literature, and adds new features, allowing modeling the business requirements of today's transport companies. This new integrated model solves the problem of defining the flights timetable, the problem of assigning the type of vehicle, and the problem of creating aircraft rotations. The problem has been solved by linear programming and ACO. This problem has been developed with the ILP and LME frameworks, and is available in the SaaS platform. The last problem discussed in the thesis is the tactical planning staff problem (TWFP), which is to define the staff of lower cost, to cover a given work load. It has been defined a very rich problem model in the definition of contracts, allowing the use of the model in various productive sectors. It has been defined a linear programming mathematical model to represent the problem. Some use cases has been defined, to show the versatility of the model problem, and to simulate the decision making process of setting up a staff, economically quantifying every decision that is made. This problem has been developed with the ILP framework, and is available in the SaaS platform.

Exploring ant-based algorithms for gene expression data analysis

Objective: Recently, much research has been proposed using nature inspired algorithms to perform complex machine learning tasks. Ant colony optimization (ACO) is one such algorithm based on swarm intelligence and is derived from a model inspired by the collective foraging behavior of ants. Taking advantage of the ACO in traits such as self-organization and robustness, this paper investigates ant-based algorithms for gene expression data clustering and associative classification. Methods and material: An ant-based clustering (Ant-C) and an ant-based association rule mining (Ant-ARM) algorithms are proposed for gene expression data analysis. The proposed algorithms make use of the natural behavior of ants such as cooperation and adaptation to allow for a flexible robust search for a good candidate solution. Results: Ant-C has been tested on the three datasets selected from the Stanford Genomic Resource Database and achieved relatively high accuracy compared to other classical clustering methods. Ant-ARM has been tested on the acute lymphoblastic leukemia (ALL)/acute myeloid leukemia (AML) dataset and generated about 30 classification rules with high accuracy. Conclusions: Ant-C can generate optimal number of clusters without incorporating any other algorithms such as K-means or agglomerative hierarchical clustering. For associative classification, while a few of the well-known algorithms such as Apriori, FP-growth and Magnum Opus are unable to mine any association rules from the ALL/AML dataset within a reasonable period of time, Ant-ARM is able to extract associative classification rules.

Prognóstico de falhas baseado em redes neurais com estados de eco

Tese (mestrado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Mecânica, 2015.

Intrinsic colony conditions affect the provisioning and oviposition process in the stingless bee Melipona scutellaris

The cell provisioning and oviposition process (POP) is a unique characteristic of stingless bees (Meliponini), in which coordinated interactions between workers and queen regulate the filling of brood cells with larval resources and subsequent egg laying. Environmental conditions seem to regulate reproduction in stingless bees; however, little is known about how the amount of food affects quantitative sequences of the process. We examined intrinsic variables by comparing three colonies in distinct conditions (strong, intermediate and weak state). We predicted that some of these variables are correlated with temporal events of POP in Melipona scutellaris colonies. The results demonstrated that the strong colony had shorter periods of POP.

First discovery of a rare polygyne colony in the stingless bee Melipona quadrifasciata (Apidae, Meliponini)

FAPESP

Impact of helpers on colony productivity in a primitively eusocial bee

Small societies of totipotent individuals are good systems in which to study the costs and benefits of group living that are central to the origin and maintenance of eusociality. For instance, in eusocial halictid bees, some females remain in their natal nest to help rear the next brood. Why do helpers stay in the nest? Do they really help, and if yes, is their contribution large enough to voluntarily forfeit direct reproduction? Here, we estimate the impact of helpers on colony survival and productivity in the sweat bee Halictus scabiosae. The number of helpers was positively associated with colony survival and productivity. Colonies from which we experimentally removed one helper produced significantly fewer offspring. However, the effect of helper removal was very small, on average. From the removal experiment, we estimated that one helper increased colony productivity by 0.72 additional offspring in colonies with one to three helpers, while the increase was smaller and not statistically significant in larger colonies. We conclude that helpers do actually help in this primitively eusocial bee, particularly in small colonies. However, the resulting increase in colony productivity is low, which suggests that helpers may be constrained in their role or may attempt to reproduce.

A morphologically specialized soldier caste improves colony defense in a neotropical eusocial bee

Division of labor among workers is common in insect societies and is thought to be important in their ecological success. In most species, division of labor is based on age (temporal castes), but workers in some ants and termites show morphological specialization for particular tasks (physical castes). Large-headed soldier ants and termites are well-known examples of this specialization. However, until now there has been no equivalent example of physical worker subcastes in social bees or wasps. Here we provide evidence for a physical soldier subcaste in a bee. In the neotropical stingless bee Tetragonisca angustula, nest defense is performed by two groups of guards, one hovering near the nest entrance and the other standing on the wax entrance tube. We show that both types of guards are 30% heavier than foragers and of different shape; foragers have relatively larger heads, whereas guards have larger legs. Low variation within each subcaste results in negligible size overlap between guards and foragers, further indicating that they are distinct physical castes. In addition, workers that remove garbage from the nest are of intermediate size, suggesting that they might represent another unrecognized caste. Guards or soldiers are reared in low but sufficient numbers (1-2% of emerging workers), considering that <1% usually perform this task. When challenged by the obligate robber bee Lestrimelitta limao, an important natural enemy, larger workers were able to fight for longer before being defeated by the much larger robber. This discovery opens up opportunities for the comparative study of physical castes in social insects, including the question of why soldiers appear to be so much rarer in bees than in ants or termites.