Shortest Route at Dynamic Location with Node Combination-Dijkstra Algorithm

Abstract— Online transportation has become a basic requirement of the general public in support of all activities to go to work, school or vacation to the sights. Public transportation services compete to provide the best service so that consumers feel comfortable using the services offered, so that all activities are noticed, one of them is the search for the shortest route in picking the buyer or delivering to the destination. Node Combination method can minimize memory usage and this methode is more optimal when compared to A* and Ant Colony in the shortest route search like Dijkstra algorithm, but can’t store the history node that has been passed. Therefore, using node combination algorithm is very good in searching the shortest distance is not the shortest route. This paper is structured to modify the node combination algorithm to solve the problem of finding the shortest route at the dynamic location obtained from the transport fleet by displaying the nodes that have the shortest distance and will be implemented in the geographic information system in the form of map to facilitate the use of the system. Keywords— Shortest Path, Algorithm Dijkstra, Node Combination, Dynamic Location (key words

Task Allocation in Foraging Robot Swarms:The Role of Information Sharing

Autonomous task allocation is a desirable feature of robot swarms that collect and deliver items in scenarios where congestion, caused by accumulated items or robots, can temporarily interfere with swarm behaviour. In such settings, self-regulation of workforce can prevent unnecessary energy consumption. We explore two types of self-regulation: non-social, where robots become idle upon experiencing congestion, and social, where robots broadcast information about congestion to their team mates in order to socially inhibit foraging. We show that while both types of self-regulation can lead to improved energy efficiency and increase the amount of resource collected, the speed with which information about congestion flows through a swarm affects the scalability of these algorithms

Mapping neural responses onto innate and acquired behavior: from insect olfaction to realizing a bio-hybrid chemical recognition system

In many organisms, the sense of smell, driven by the olfactory system, serves as the primary sensory modality that guides a plethora of behaviors such as foraging for food, finding mates, and evading predators. Using an array of biological sensors, the olfactory system converts volatile chemical inputs from an organism’s environment into well-patterned neural responses that inform downstream motor neurons to drive appropriate behaviors (e.g., moving towards food or away from danger). For many external cues, the elicited neural responses are often determined by the genetic makeup of the organism, which assigns an innate preference, or valence, for these different stimuli. However, our environment is constantly in flux, and the same stimulus can be encountered in a variety of different contexts, such as following other cues or under different ambient conditions (e.g., humidity). This can modify the neural activation pattern ascribed to the stimulus and potentially alter the corresponding behavioral output. The objective of this dissertation is to understand how neural responses in the early olfactory system of locusts (Schisctocerca americana) are spatiotemporally structured to robustly represent innate valence in different scenarios to drive appropriate behaviors and how they can be altered through learning. To achieve this goal, we used a large panel of chemically diverse odorants and characterized the neural responses they elicited in the antennal lobe (at the level of ensembles of principal or projection neurons) as well as the innate appetitive behavioral response they produced. We found that neural responses generated both during (ON response) and after (OFF response) termination of the odorant contained information regarding its identity and could be used to predict the innate behavioral outcomes. Notably, predictions made using the ON and the OFF responses differed in the sets of neurons they used to generate the predictions, indicating that neural-behavioral transformations could be achieved in multiple ways. Furthermore, both these ON and OFF neural response classifiers outperformed attempts to predict behavior using chemical features of the stimuli (detected by NMR or IR spectra), indicating that the antennal lobe was transforming and encoding olfactory inputs to map them onto the innate valence associated with the sensory cue. We found that the organization of odor-evoked neural responses that readily map onto innate preferences may also constrain learned odor-reward associations. While odorants with an innate positive behavioral preference alone could support learning odor-reward associations, the conditioned responses were not odor-specific but appeared to generalize to other odorants that evoked similar neural responses. The timing of the behavioral responses could be varied by delivering rewards during epochs when the odorant would generate either the ON or the OFF neural responses. Overall, we found that the organization of ON and OFF neural responses in the antennal lobe clustered into manifolds or subspaces that could be explained using innate behavioral preferences and suitability for reinforcement learning. To understand the robustness of these results, we developed novel minimally invasive experimental methods to record locust neural responses while they actively sampled their surroundings. We found neural responses in this more naturalistic scenario to maintain their manifold organization, and classical conditioning enhanced the separation between neural responses evoked by innately appetitive and non-appetitive odorants. Our results also indicate that neural and behavioral responses in freely moving locusts were consistent with those observed earlier in highly compromised preparations. Finally, we exploited our newly-developed recording techniques to engineer an insect-based chemical sensor that could be used for a real-world application

Impacts des polluants métalliques sur l'abeille : de la colonie au cerveau

Les abeilles sont des pollinisateurs essentiels. Une pléthore de facteurs de stress environnementaux, tels que les produits agrochimiques, a été identifiée comme contribuant à leur déclin mondial. En particulier, ces facteurs de stress altèrent les processus cognitifs impliqués dans les comportements fondamentaux. Jusqu'à présent, cependant, on ne sait pratiquement rien de l'impact de l'exposition à des métaux lourds, dont la toxicité est avérée chez de nombreux organismes. Pourtant, leurs émissions mondiales résultant des activités humaines ont élevé leurs concentrations bien au-dessus des niveaux naturels dans l'air, le sol, l'eau et la flore, exposant ainsi les abeilles à tous les stades de leur vie. Le but de ma thèse était d'examiner les effets de la pollution métallique sur l'abeille domestique en utilisant une approche multi-échelle, du cerveau à la colonie, en laboratoire et sur le terrain. J'ai d'abord observé que les abeilles exposées à une gamme de concentrations de trois métaux communs (arsenic, plomb et zinc) en laboratoire étaient incapables de percevoir et éviter des concentrations usuelles, néanmoins nocives, de ces métaux dans leur nourriture. J'ai ensuite exposé de façon chronique des colonies à des concentrations réalistes de plomb dans la nourriture et démontré que la consommation de ce métal altérait la cognition et le développement morphologique des abeilles. Comme les polluants métalliques se trouvent souvent dans des mélanges complexes dans l'environnement, j'ai exploré l'effet des cocktails de métaux, montrant que l'exposition au plomb, à l'arsenic ou au cuivre seul était suffisante pour ralentir l'apprentissage et perturber le rappel de la mémoire, et que les combinaisons de ces métaux induisaient des effets négatifs additifs sur ces deux processus cognitifs. J'ai finalement étudié l'impact de l'exposition naturelle aux polluants métalliques dans un environnement contaminé, en collectant des abeilles à proximité d'une ancienne mine d'or, et montré que les individus des populations les plus exposées aux métaux présentaient des capacités d'apprentissage et de mémoire plus faibles, et des altérations de leur développement conduisant à une réduction de la taille de leur cerveau. Une analyse plus systématique des abeilles non exposées a révélé une relation entre la taille de la tête, la morphométrie du cerveau et les performances d'apprentissage dans différentes tâches comportementales, suggérant que l'exposition aux polluants métalliques amplifie ces variations naturelles. Ainsi, mes résultats suggèrent que les abeilles domestiques sont incapables d'éviter l'exposition à des concentrations réalistes de métaux qui sont préjudiciables au développement et aux fonctions cognitives, et appellent à une révision des niveaux environnementaux considérés comme "sûrs". Ma thèse est la première analyse intégrée de l'impact de plusieurs polluants métalliques sur la cognition, la morphologie et l'organisation cérébrale chez l'abeille, et vise à encourager de nouvelles études sur la contribution de la pollution métallique dans le déclin signalé des abeilles, et plus généralement, des insectes.Honey bees are crucial pollinators. A plethora of environmental stressors, such as agrochemicals, have been identified as contributors to their global decline. Especially, these stressors impair cognitive processes involved in fundamental behaviours. So far however, virtually nothing is known about the impact of metal pollutants, despite their known toxicity to many organisms. Their worldwide emissions resulting from human activities have elevated their concentrations far above natural baselines in the air, soil, water and flora, exposing bees at all life stages. The aim of my thesis was to examine the effects of metallic pollution on honey bees using a multiscale approach, from brain to colonies, in laboratory and field conditions. I first observed that bees exposed to a range of concentrations of three common metals (arsenic, lead and zinc) in the laboratory were unable to perceive and avoid, low, yet harmful, field-realistic concentrations of those metals in their food. I then chronically exposed colonies to field-realistic concentrations of lead in food and demonstrated that consumption of this metal impaired bee cognition and morphological development, leading to smaller adult bees. As metal pollutants are often found in complex mixtures in the environment, I explored the effect of cocktails of metals, showing that exposure to lead, arsenic or copper alone was sufficient to slow down learning and disrupt memory retrieval, and that combinations of these metals induced additive negative effects on both cognitive processes. I finally investigated the impact of natural exposure to metal pollutants in a contaminated environment, by collecting bees in the vicinity of a former gold mine, and showed that individuals from populations most exposed to metals exhibited lower learning and memory abilities, and development impairments conducing to reduced brain size. A more systematic analysis of unexposed bees revealed a relationship between head size, brain morphometrics and learning performances in different behavioural tasks, suggesting that exposure to metal pollutants magnifies these natural variations. Hence, altogether, my results suggest that honey bees are unable to avoid exposure to field-realistic concentrations of metals that are detrimental to development and cognitive functions; and call for a revision of the environmental levels considered as 'safe'. My thesis is the first integrated analysis of the impact of several metal pollutants on bee cognition, morphology and brain structure, and should encourage further studies on the contribution of metal pollution in the reported decline of honey bees, and more generally, of insects