852 resultados para Open adaptation. Self-adaptation. Components. OSGi


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Based on a case study of Charazani – Bolivia, this article outlines the understanding of adaptive strategies to cope with climate change and its impact on environmental and socioeconomic conditions that are affecting rural livelihoods. Mainly qualitative methods were used to collect and analyze data following the framework for vulnerability assessments of a socio-ecological system. Climate data reveals an increase of precipitation and temperature during the last decades. Furthermore the occurrence of extreme weather events, particularly drought, frost, hailstorms and consequently landslides and fire are increasing. Local testimonies highlight these events as the principle reasons for agricultural losses. This climatic variability and simultaneous social changes were identified as the drivers of vulnerability. Yet, several adaptive measures were identified at household, community and external levels in order to cope with such vulnerability; e.g. traditional techniques in agriculture and risk management. Gradually, farmers complement these activities with contemporary practices in agriculture, like intensification of land use, diversification of irrigation system and use of artificial fertilizers. As part of a recent trend community members are forced to search for new off-farm alternatives beyond agriculture for subsistence. Despite there is a correspondingly large array of possible adaptation measures that families are implementing, local testimonies point out, that farmers often do not have the capacity and neither the economical resources to mitigate the risk in agricultural production. Although several actions are already considered to promote further adaptive capacity, the current target is to improve existing livelihood strategies by reducing vulnerability to hazards induced by climate change.

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Self-adaptive software provides a profound solution for adapting applications to changing contexts in dynamic and heterogeneous environments. Having emerged from Autonomic Computing, it incorporates fully autonomous decision making based on predefined structural and behavioural models. The most common approach for architectural runtime adaptation is the MAPE-K adaptation loop implementing an external adaptation manager without manual user control. However, it has turned out that adaptation behaviour lacks acceptance if it does not correspond to a user’s expectations – particularly for Ubiquitous Computing scenarios with user interaction. Adaptations can be irritating and distracting if they are not appropriate for a certain situation. In general, uncertainty during development and at run-time causes problems with users being outside the adaptation loop. In a literature study, we analyse publications about self-adaptive software research. The results show a discrepancy between the motivated application domains, the maturity of examples, and the quality of evaluations on the one hand and the provided solutions on the other hand. Only few publications analysed the impact of their work on the user, but many employ user-oriented examples for motivation and demonstration. To incorporate the user within the adaptation loop and to deal with uncertainty, our proposed solutions enable user participation for interactive selfadaptive software while at the same time maintaining the benefits of intelligent autonomous behaviour. We define three dimensions of user participation, namely temporal, behavioural, and structural user participation. This dissertation contributes solutions for user participation in the temporal and behavioural dimension. The temporal dimension addresses the moment of adaptation which is classically determined by the self-adaptive system. We provide mechanisms allowing users to influence or to define the moment of adaptation. With our solution, users can have full control over the moment of adaptation or the self-adaptive software considers the user’s situation more appropriately. The behavioural dimension addresses the actual adaptation logic and the resulting run-time behaviour. Application behaviour is established during development and does not necessarily match the run-time expectations. Our contributions are three distinct solutions which allow users to make changes to the application’s runtime behaviour: dynamic utility functions, fuzzy-based reasoning, and learning-based reasoning. The foundation of our work is a notification and feedback solution that improves intelligibility and controllability of self-adaptive applications by implementing a bi-directional communication between self-adaptive software and the user. The different mechanisms from the temporal and behavioural participation dimension require the notification and feedback solution to inform users on adaptation actions and to provide a mechanism to influence adaptations. Case studies show the feasibility of the developed solutions. Moreover, an extensive user study with 62 participants was conducted to evaluate the impact of notifications before and after adaptations. Although the study revealed that there is no preference for a particular notification design, participants clearly appreciated intelligibility and controllability over autonomous adaptations.

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Poor adaptation to climate change is a major threat to sustainable rice production in Nigeria. Determinants of appropriate climate-change adaptation strategies used by rice farmers in Southwestern Nigeria have not been fully investigated. In this study, the determinants of climate change adaptation strategies used by rice farmers in Southwestern Nigeria were investigated. Data were obtained through Focus Group Discussions (FGDs) and field survey conducted in the study areas. Data obtained were analyzed using descriptive and inferential statistical tools such as percentage and regression analysis. The major climate change adaptation strategies used by the respondents included; planting improved rice variety such as Federal Agricultural Research Oryza (FARO) (80.5 %), seeking early warning information (80.9 %), shifting planting date until the weather condition was favourable (99.1 %), and using chemical fertilizer on their farms in order to maintain soil fertility (20.5 %). The determinants of climate change adaptation strategies used by the farmers, included access to early warning information (β=43.04), access to fertilizer (β=5.78), farm plot size (β=–12.04) and access to regular water supply (β=–24.79). Climate change adaptation required provision of incentives to farmers, training on drought and flood control, and the use of improved technology to obtain higher yield.

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Investing in global environmental and adaptation benefits in the context of agriculture and food security initiatives can play an important role in promoting sustainable intensification. This is a priority for the Global Environment Facility (GEF), created in 1992 with a mandate to serve as financial mechanism of several multilateral environmental agreements. To demonstrate the nature and extent of GEF financing, we conducted an assessment of the entire portfolio over a period of two decades (1991–2011) to identify projects with direct links to agriculture and food security. A cohort of 192 projects and programs were identified and used as a basis for analyzing trends in GEF financing. The projects and programs together accounted for a total GEF financing of US$1,086.8 million, and attracted an additional US$6,343.5 million from other sources. The value-added of GEF financing for ecosystem services and resilience in production systems was demonstrated through a diversity of interventions in the projects and programs that utilized US$810.6 million of the total financing. The interventions fall into the following four main categories in accordance with priorities of the GEF: sustainable land management (US$179.3 million), management of agrobiodiversity (US$113.4 million), sustainable fisheries and water resource management (US$379.8 million), and climate change adaptation (US$138.1 million). By aligning GEF priorities with global aspirations for sustainable intensification of production systems, the study shows that it is possible to help developing countries tackle food insecurity while generating global environmental benefits for a healthy and resilient planet.

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Aquest quadern és el primer lliurement de les Orientacions per a l’adaptació a l’espai europeu d’educació superior. Té l’origen en el debat de la Comissió de seguiment del Pla pilot d’adaptació a l’espai europeu d’educació superior de la UdG i del grup de treball que s’ha constituït l’hivern 2005-2006 expressament per tractar el tema de les competències

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Aquest quadern és el quart lliurement de la Guia per a l'adaptació a l'espai europeo superior. Té l'origen en el debat de la Comissió de Seguiment del Pla Pilot d'adaptació a l'Espai Europeu d'Educació Superior de la UdG i del grup de treball que s'ha constituït l'estiu del 2006 expressament per tractar el tema de les activitats d'aprenentatge

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Aquest quadern és el cinquè lliurement de la Guia per a l'adaptació a l'espai europeu d'educació superior. Té l'origen en el debat de la Comissió de Seguiment del Pla pilot d'Adaptació a l'Espai Europeu d'Educació Superior de la UdG i del grup de treball que s'ha constituït expressament per tractar el tema de l'avaluació dels aprenentatges

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Aquest quadern és el sisè lliurament de la Guia per a l'adaptació a l'espai europeu d'educació superior. Té l'origen en el debat de la Comissió de Seguiment del Pla Pilot d'Adaptació a l'Espai Europeu d'Educació Superior de la UdG i del grup de treball que s'ha constituït expressament per tractar el tema de l'avaluació dels aprenentatges

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Self-organizing maps (Kohonen 1997) is a type of artificial neural network developed to explore patterns in high-dimensional multivariate data. The conventional version of the algorithm involves the use of Euclidean metric in the process of adaptation of the model vectors, thus rendering in theory a whole methodology incompatible with non-Euclidean geometries. In this contribution we explore the two main aspects of the problem: 1. Whether the conventional approach using Euclidean metric can shed valid results with compositional data. 2. If a modification of the conventional approach replacing vectorial sum and scalar multiplication by the canonical operators in the simplex (i.e. perturbation and powering) can converge to an adequate solution. Preliminary tests showed that both methodologies can be used on compositional data. However, the modified version of the algorithm performs poorer than the conventional version, in particular, when the data is pathological. Moreover, the conventional ap- proach converges faster to a solution, when data is \well-behaved". Key words: Self Organizing Map; Artificial Neural networks; Compositional data

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Resumen tomado de la publicaci??n. Resumen tambi??n en ingl??s

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In this paper we present a novel approach to assigning roles to robots in a team of physical heterogeneous robots. Its members compete for these roles and get rewards for them. The rewards are used to determine each agent’s preferences and which agents are better adapted to the environment. These aspects are included in the decision making process. Agent interactions are modelled using the concept of an ecosystem in which each robot is a species, resulting in emergent behaviour of the whole set of agents. One of the most important features of this approach is its high adaptability. Unlike some other learning techniques, this approach does not need to start a whole exploitation process when the environment changes. All this is exemplified by means of experiments run on a simulator. In addition, the algorithm developed was applied as applied to several teams of robots in order to analyse the impact of heterogeneity in these systems

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Resumen tomado de la publicaci??n

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Our work is focused on alleviating the workload for designers of adaptive courses on the complexity task of authoring adaptive learning designs adjusted to specific user characteristics and the user context. We propose an adaptation platform that consists in a set of intelligent agents where each agent carries out an independent adaptation task. The agents apply machine learning techniques to support the user modelling for the adaptation process