To Develop a Database Management Tool for Multi-Agent Simulation Platform

Depuis peu, la Modélisation et Simulation par Agents (ABMs) est passée d'une approche dirigée par les modèles à une approche dirigée par les données (Data Driven Approach, DDA). Cette tendance vers l’utilisation des données dans la simulation vise à appliquer les données collectées par les systèmes d’observation à la simulation (Edmonds and Moss, 2005; Hassan, 2009). Dans la DDA, les données empiriques collectées sur les systèmes cibles sont utilisées non seulement pour la simulation des modèles mais aussi pour l’initialisation, la calibration et l’évaluation des résultats issus des modèles de simulation, par exemple, le système d’estimation et de gestion des ressources hydrauliques du bassin Adour-Garonne Français (Gaudou et al., 2013) et l’invasion des rizières du delta du Mékong au Vietnam par les cicadelles brunes (Nguyen et al., 2012d). Cette évolution pose la question du « comment gérer les données empiriques et celles simulées dans de tels systèmes ». Le constat que l’on peut faire est que, si la conception et la simulation actuelles des modèles ont bénéficié des avancées informatiques à travers l’utilisation des plateformes populaires telles que Netlogo (Wilensky, 1999) ou GAMA (Taillandier et al., 2012), ce n'est pas encore le cas de la gestion des données, qui sont encore très souvent gérées de manière ad-hoc. Cette gestion des données dans des Modèles Basés Agents (ABM) est une des limitations actuelles des plateformes de simulation multiagents (SMA). Autrement dit, un tel outil de gestion des données est actuellement requis dans la construction des systèmes de simulation par agents et la gestion des bases de données correspondantes est aussi un problème important de ces systèmes. Dans cette thèse, je propose tout d’abord une structure logique pour la gestion des données dans des plateformes de SMA. La structure proposée qui intègre des solutions de l’Informatique Décisionnelle et des plateformes multi-agents s’appelle CFBM (Combination Framework of Business intelligence and Multi-agent based platform), elle a plusieurs objectifs : (1) modéliser et exécuter des SMAs, (2) gérer les données en entrée et en sortie des simulations, (3) intégrer les données de différentes sources, et (4) analyser les données à grande échelle. Ensuite, le besoin de la gestion des données dans les simulations agents est satisfait par une implémentation de CFBM dans la plateforme GAMA. Cette implémentation présente aussi une architecture logicielle pour combiner entrepôts deIv données et technologies du traitement analytique en ligne (OLAP) dans les systèmes SMAs. Enfin, CFBM est évaluée pour la gestion de données dans la plateforme GAMA à travers le développement de modèles de surveillance des cicadelles brunes (BSMs), où CFBM est utilisé non seulement pour gérer et intégrer les données empiriques collectées depuis le système cible et les résultats de simulation du modèle simulé, mais aussi calibrer et valider ce modèle. L'intérêt de CFBM réside non seulement dans l'amélioration des faiblesses des plateformes de simulation et de modélisation par agents concernant la gestion des données mais permet également de développer des systèmes de simulation complexes portant sur de nombreuses données en entrée et en sortie en utilisant l’approche dirigée par les données.Recently, there has been a shift from modeling driven approach to data driven approach inAgent Based Modeling and Simulation (ABMS). This trend towards the use of data-driven approaches in simulation aims at using more and more data available from the observation systems into simulation models (Edmonds and Moss, 2005; Hassan, 2009). In a data driven approach, the empirical data collected from the target system are used not only for the design of the simulation models but also in initialization, calibration and evaluation of the output of the simulation platform such as e.g., the water resource management and assessment system of the French Adour-Garonne Basin (Gaudou et al., 2013) and the invasion of Brown Plant Hopper on the rice fields of Mekong River Delta region in Vietnam (Nguyen et al., 2012d). That raises the question how to manage empirical data and simulation data in such agentbased simulation platform. The basic observation we can make is that currently, if the design and simulation of models have benefited from advances in computer science through the popularized use of simulation platforms like Netlogo (Wilensky, 1999) or GAMA (Taillandier et al., 2012), this is not yet the case for the management of data, which are still often managed in an ad hoc manner. Data management in ABM is one of limitations of agent-based simulation platforms. Put it other words, such a database management is also an important issue in agent-based simulation systems. In this thesis, I first propose a logical framework for data management in multi-agent based simulation platforms. The proposed framework is based on the combination of Business Intelligence solution and a multi-agent based platform called CFBM (Combination Framework of Business intelligence and Multi-agent based platform), and it serves several purposes: (1) model and execute multi-agent simulations, (2) manage input and output data of simulations, (3) integrate data from different sources; and (4) analyze high volume of data. Secondly, I fulfill the need for data management in ABM by the implementation of CFBM in the GAMA platform. This implementation of CFBM in GAMA also demonstrates a software architecture to combine Data Warehouse (DWH) and Online Analytical Processing (OLAP) technologies into a multi-agent based simulation system. Finally, I evaluate the CFBM for data management in the GAMA platform via the development of a Brown Plant Hopper Surveillance Models (BSMs), where CFBM is used ii not only to manage and integrate the whole empirical data collected from the target system and the data produced by the simulation model, but also to calibrate and validate the models.The successful development of the CFBM consists not only in remedying the limitation of agent-based modeling and simulation with regard to data management but also in dealing with the development of complex simulation systems with large amount of input and output data supporting a data driven approach

To Develop a Database Management Tool for Multi-Agent Simulation Platform

Depuis peu, la Modélisation et Simulation par Agents (ABMs) est passée d'une approche dirigée par les modèles à une approche dirigée par les données (Data Driven Approach, DDA). Cette tendance vers l’utilisation des données dans la simulation vise à appliquer les données collectées par les systèmes d’observation à la simulation (Edmonds and Moss, 2005; Hassan, 2009). Dans la DDA, les données empiriques collectées sur les systèmes cibles sont utilisées non seulement pour la simulation des modèles mais aussi pour l’initialisation, la calibration et l’évaluation des résultats issus des modèles de simulation, par exemple, le système d’estimation et de gestion des ressources hydrauliques du bassin Adour-Garonne Français (Gaudou et al., 2013) et l’invasion des rizières du delta du Mékong au Vietnam par les cicadelles brunes (Nguyen et al., 2012d). Cette évolution pose la question du « comment gérer les données empiriques et celles simulées dans de tels systèmes ». Le constat que l’on peut faire est que, si la conception et la simulation actuelles des modèles ont bénéficié des avancées informatiques à travers l’utilisation des plateformes populaires telles que Netlogo (Wilensky, 1999) ou GAMA (Taillandier et al., 2012), ce n'est pas encore le cas de la gestion des données, qui sont encore très souvent gérées de manière ad-hoc. Cette gestion des données dans des Modèles Basés Agents (ABM) est une des limitations actuelles des plateformes de simulation multiagents (SMA). Autrement dit, un tel outil de gestion des données est actuellement requis dans la construction des systèmes de simulation par agents et la gestion des bases de données correspondantes est aussi un problème important de ces systèmes. Dans cette thèse, je propose tout d’abord une structure logique pour la gestion des données dans des plateformes de SMA. La structure proposée qui intègre des solutions de l’Informatique Décisionnelle et des plateformes multi-agents s’appelle CFBM (Combination Framework of Business intelligence and Multi-agent based platform), elle a plusieurs objectifs : (1) modéliser et exécuter des SMAs, (2) gérer les données en entrée et en sortie des simulations, (3) intégrer les données de différentes sources, et (4) analyser les données à grande échelle. Ensuite, le besoin de la gestion des données dans les simulations agents est satisfait par une implémentation de CFBM dans la plateforme GAMA. Cette implémentation présente aussi une architecture logicielle pour combiner entrepôts deIv données et technologies du traitement analytique en ligne (OLAP) dans les systèmes SMAs. Enfin, CFBM est évaluée pour la gestion de données dans la plateforme GAMA à travers le développement de modèles de surveillance des cicadelles brunes (BSMs), où CFBM est utilisé non seulement pour gérer et intégrer les données empiriques collectées depuis le système cible et les résultats de simulation du modèle simulé, mais aussi calibrer et valider ce modèle. L'intérêt de CFBM réside non seulement dans l'amélioration des faiblesses des plateformes de simulation et de modélisation par agents concernant la gestion des données mais permet également de développer des systèmes de simulation complexes portant sur de nombreuses données en entrée et en sortie en utilisant l’approche dirigée par les données.Recently, there has been a shift from modeling driven approach to data driven approach inAgent Based Modeling and Simulation (ABMS). This trend towards the use of data-driven approaches in simulation aims at using more and more data available from the observation systems into simulation models (Edmonds and Moss, 2005; Hassan, 2009). In a data driven approach, the empirical data collected from the target system are used not only for the design of the simulation models but also in initialization, calibration and evaluation of the output of the simulation platform such as e.g., the water resource management and assessment system of the French Adour-Garonne Basin (Gaudou et al., 2013) and the invasion of Brown Plant Hopper on the rice fields of Mekong River Delta region in Vietnam (Nguyen et al., 2012d). That raises the question how to manage empirical data and simulation data in such agentbased simulation platform. The basic observation we can make is that currently, if the design and simulation of models have benefited from advances in computer science through the popularized use of simulation platforms like Netlogo (Wilensky, 1999) or GAMA (Taillandier et al., 2012), this is not yet the case for the management of data, which are still often managed in an ad hoc manner. Data management in ABM is one of limitations of agent-based simulation platforms. Put it other words, such a database management is also an important issue in agent-based simulation systems. In this thesis, I first propose a logical framework for data management in multi-agent based simulation platforms. The proposed framework is based on the combination of Business Intelligence solution and a multi-agent based platform called CFBM (Combination Framework of Business intelligence and Multi-agent based platform), and it serves several purposes: (1) model and execute multi-agent simulations, (2) manage input and output data of simulations, (3) integrate data from different sources; and (4) analyze high volume of data. Secondly, I fulfill the need for data management in ABM by the implementation of CFBM in the GAMA platform. This implementation of CFBM in GAMA also demonstrates a software architecture to combine Data Warehouse (DWH) and Online Analytical Processing (OLAP) technologies into a multi-agent based simulation system. Finally, I evaluate the CFBM for data management in the GAMA platform via the development of a Brown Plant Hopper Surveillance Models (BSMs), where CFBM is used ii not only to manage and integrate the whole empirical data collected from the target system and the data produced by the simulation model, but also to calibrate and validate the models.The successful development of the CFBM consists not only in remedying the limitation of agent-based modeling and simulation with regard to data management but also in dealing with the development of complex simulation systems with large amount of input and output data supporting a data driven approach

Flight behaviour and migration of insect pests: Radar studies in developing countries (NRI Bulletin 71)

The use of radar to make direct observations of insects flying at altitude has provided many new insights into the phenomenon of long-range insect migration. In particular, the technique has produced a wealth of quantitative information on the spatial and temporal distribution of migrants in the air, on the direction, speed and duration of their displacements, and on their orientation behaviour. These data could not have been obtained by any other means, and it is probably fair to claim that our present knowledge of the magnitude and importance of high altitude insect movement stems very largely from radar observations. The pioneering field studies using the first specially designed entomological radar were undertaken in 1968, with the support of the UK Overseas Development Administration (ODA), and since that time ODA has been responsible for funding almost all of the applications of the technique in developing countries. The motivation for this work was the assumption that it was impossible to design efficient management strategies for migrant pest insects without a good knowledge of their migratory behaviour, and of the role which this played in their population dynamics. The ODA-funded studies thus focused primarily on pest species, and were carried out by the Radar Entomology Unit of the Natural Resources Institute (NRI) and its precursors. In this Bulletin, we give a brief account of the history of radar entomology, with emphasis on studies of insect pests. Next, the different types of entomological radar and some associated analysis methods are outlined, together with descriptions of some ancillary measurement techniques. We then describe in some detail the contributions made by the NRI Radar Unit to current knowledge of the flight patterns of a variety of major insect pests of agriculture and of human health. These pests include: grasshoppers and locusts, the African Armyworm moth, the Rice Brown Planthopper and other rice pests, the Old World Bollworm, and some mosquito vectors of human diseases. Recent developments directed towards long-term monitoring of insect aerial faunas (for environmental impact, biodiversity and conservation purposes), and towards observations of low-altitude flight, are included. The Bulletin concludes with a short overview, in which we speculate how the technique might find application in the future

Area-wide Integrated Pest Management

Extensive reliance on insecticides reduces biodiversity, contributes to pollinator decline, destroys habitat and threatens endangered species. This book offers a more effective application of the Integrated Pest Management (IPM) approach, on an area-wide (AW) or population-wide (AW-IPM) basis. It addresses the importance of problem-solving research, planning and baseline data collection, integrating tools for appropriate control strategies, and pilot trials. The 48 chapters authored by 184 experts cover advances in genetics, molecular biology, biological control, resistance management, modelling, automated surveillance and unmanned aerial release systems

Climate Neutral and Resilient Farming Systems

This book presents evidence-based research on climate-neutral and resilient farming systems and further to provide innovative and practical solutions for reducing greenhouse gas emissions and mitigating the impact of climate change. Intensive farming systems are a significant source of greenhouse gas emissions, thereby contributing to global warming and the acceleration of climate change. As paddy rice farming is one of the largest contributors, and most environmentally damaging farming systems, this will be a particular focus of the book. The mitigation of greenhouse gas emissions needs to be urgently addressed to achieve the 2 degrees Celsius target adopted by COP21 and the 2015 Paris Agreement, but this is not possible if local and national level innovations are not accompanied by international level cooperation, mutual learning and sharing of knowledge and technologies. This book, therefore, brings together international collaborative research on climate-neutral and resilient farming systems compiled by leading scientists and experts from Europe, Asia and Africa. The chapters present evidence-based research and innovative solutions that can be applied or upscaled in different farming systems and regions across the world. Chapters present models and technologies that can be used for practical implementation at the systemic level and advance state of the art knowledge on carbon neutral farming. Combining theory and practice, this interdisciplinary book provides guidance which can inform and increase cooperation between researchers from various countries on climate-neutral and resilient farming systems. Most importantly, the volume provides recommendations which can be put into practice by those working in the agricultural industry, especially in developing countries, where they are attempting to promote climate-neutral and resilient farming systems. The book will be of great interest to students and academics of sustainable agriculture, food security, climate mitigation and sustainable development, in addition to policymakers and practitioners working in these areas

Area-wide Integrated Pest Management

Over 98% of sprayed insecticides and 95% of herbicides reach a destination other than their target species, including non-target species, air, water and soil. The extensive reliance on insecticide use reduces biodiversity, contributes to pollinator decline, destroys habitat, and threatens endangered species. This book offers a more effective application of the Integrated Pest Management (IPM) approach, on an area-wide (AW) or population-wide (AW-IPM) basis, which aims at the management of the total population of a pest, involving a coordinated effort over often larger areas. For major livestock pests, vectors of human diseases and pests of high-value crops with low pest tolerance, there are compelling economic reasons for participating in AW-IPM. This new textbook attempts to address various fundamental components of AW-IPM, e.g. the importance of relevant problem-solving research, the need for planning and essential baseline data collection, the significance of integrating adequate tools for appropriate control strategies, and the value of pilot trials, etc. With chapters authored by 184 experts from more than 31 countries, the book includes many technical advances in the areas of genetics, molecular biology, microbiology, resistance management, and social sciences that facilitate the planning and implementing of area-wide strategies. The book is essential reading for the academic and applied research community as well as national and regional government plant and human/animal health authorities with responsibility for protecting plant and human/animal health

Climate Neutral and Resilient Farming Systems

This book presents evidence-based research on climate-neutral and resilient farming systems and further to provide innovative and practical solutions for reducing greenhouse gas emissions and mitigating the impact of climate change. Intensive farming systems are a significant source of greenhouse gas emissions, thereby contributing to global warming and the acceleration of climate change. As paddy rice farming is one of the largest contributors, and most environmentally damaging farming systems, this will be a particular focus of the book. The mitigation of greenhouse gas emissions needs to be urgently addressed to achieve the 2 degrees Celsius target adopted by COP21 and the 2015 Paris Agreement, but this is not possible if local and national level innovations are not accompanied by international level cooperation, mutual learning and sharing of knowledge and technologies. This book, therefore, brings together international collaborative research on climate-neutral and resilient farming systems compiled by leading scientists and experts from Europe, Asia and Africa. The chapters present evidence-based research and innovative solutions that can be applied or upscaled in different farming systems and regions across the world. Chapters present models and technologies that can be used for practical implementation at the systemic level and advance state of the art knowledge on carbon neutral farming. Combining theory and practice, this interdisciplinary book provides guidance which can inform and increase cooperation between researchers from various countries on climate-neutral and resilient farming systems. Most importantly, the volume provides recommendations which can be put into practice by those working in the agricultural industry, especially in developing countries, where they are attempting to promote climate-neutral and resilient farming systems. The book will be of great interest to students and academics of sustainable agriculture, food security, climate mitigation and sustainable development, in addition to policymakers and practitioners working in these areas

Incorporating standardised drift-tube ion mobility to enhance non-targeted assessment of the wine metabolome (LC×IM-MS)

Liquid chromatography with drift-tube ion mobility spectrometry-mass spectrometry (LCxIM-MS) is emerging as a powerful addition to existing LC-MS workflows for addressing a diverse range of metabolomics-related questions [1,2]. Importantly, excellent precision under repeatability and reproducibility conditions of drift-tube IM separations [3] supports the development of non-targeted approaches for complex metabolome assessment such as wine characterisation [4]. In this work, fundamentals of this new analytical metabolomics approach are introduced and application to the analysis of 90 authentic red and white wine samples originating from Macedonia is presented. Following measurements, intersample alignment of metabolites using non-targeted extraction and three-dimensional alignment of molecular features (retention time, collision cross section, and high-resolution mass spectra) provides confidence for metabolite identity confirmation. Applying a fingerprinting metabolomics workflow allows statistical assessment of the influence of geographic region, variety, and age. This approach is a state-of-the-art tool to assess wine chemodiversity and is particularly beneficial for the discovery of wine biomarkers and establishing product authenticity based on development of fingerprint libraries

Design and optimization of the Automatic Brown PlantHopper (BPH) light trap surveillance network

Communicating coverage of automatic BPH light trap surveillance network characterizes how well an area is monitored or tracked by automatic light traps. Connectivity is an important required that shows how nodes in a automatic BPH light trap surveillance network (BSNET) can effectively communicate. Some areas in the deployment region are more important than other areas and need to be covered. In this paper, we propose a new approach based hexagonal cellular automata to find the automatic light trap node distribution. This approach are ensure the deployment region that maximizes the coverage area of BSNET, and preserve connectivity between nodes provided

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