971 resultados para Agricultural and Biological Sciences(all)
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This paper, investigates causal relationships among agriculture, manufacturing and export in Tanzania by using time series data for the period between 1970 and 2005. The empirical results show in both sectors there is Granger causality where agriculture causes both exports and manufacturing. Exports also cause both agricultural GDP and manufacturing GDP and any two variables out of three jointly cause the third one. There is also some evidence that manufacturing does not cause export and agriculture. Regarding cointegration, pairwise agricultural GDP and export are cointegrated, export and manufacture are cointegrated. Agriculture and manufacture are cointegrated but they are lag sensitive. However, three variables, manufacturing, export and agriculture both together are cointegrated showing that they share long run relation and this has important economic implications.
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Bruise damage is a major cause of quality loss for apples. It would be very useful to establish a method of characterizing bruise susceptibility in order to improve fruit handling, sometimes Magness-Taylor firmness is used as an indirect guide to handling requirements. The objective of the present work was to achieve a better bruise susceptibility prediction.
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Nondestructive techniques are extensively researched for the measurement of physical properties of fruits related to quality. Optical properties can be applied mainly in the detection of those quality features which are related to the chemical composition of the fruit, color (in the VIS region) or chemical constituents (sugar, in the MR region) being the most important. The most relevant mechanical property of fruits is consistency, generally called firmness, and to date only techniques which are able to measure the mechanical properties of the fruit bulk tissue are used for its prediction. Fruits can be modelled as elastic bodies, or at least as partially elastic. Therefore, the measurement of some elastic constants of the fruit can be used for the evaluation of its firmness. The differences in the response to loading are relevant in studying a) fruit firmness and b) bruising susceptibility. Both have been modelled for selected fruit species and varieties.
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Emotion is generally argued to be an influence on the behavior of life systems, largely concerning flexibility and adaptivity. The way in which life systems acts in response to a particular situations of the environment, has revealed the decisive and crucial importance of this feature in the success of behaviors. And this source of inspiration has influenced the way of thinking artificial systems. During the last decades, artificial systems have undergone such an evolution that each day more are integrated in our daily life. They have become greater in complexity, and the subsequent effects are related to an increased demand of systems that ensure resilience, robustness, availability, security or safety among others. All of them questions that raise quite a fundamental challenges in control design. This thesis has been developed under the framework of the Autonomous System project, a.k.a the ASys-Project. Short-term objectives of immediate application are focused on to design improved systems, and the approaching of intelligence in control strategies. Besides this, long-term objectives underlying ASys-Project concentrate on high order capabilities such as cognition, awareness and autonomy. This thesis is placed within the general fields of Engineery and Emotion science, and provides a theoretical foundation for engineering and designing computational emotion for artificial systems. The starting question that has grounded this thesis aims the problem of emotion--based autonomy. And how to feedback systems with valuable meaning has conformed the general objective. Both the starting question and the general objective, have underlaid the study of emotion, the influence on systems behavior, the key foundations that justify this feature in life systems, how emotion is integrated within the normal operation, and how this entire problem of emotion can be explained in artificial systems. By assuming essential differences concerning structure, purpose and operation between life and artificial systems, the essential motivation has been the exploration of what emotion solves in nature to afterwards analyze analogies for man--made systems. This work provides a reference model in which a collection of entities, relationships, models, functions and informational artifacts, are all interacting to provide the system with non-explicit knowledge under the form of emotion-like relevances. This solution aims to provide a reference model under which to design solutions for emotional operation, but related to the real needs of artificial systems. The proposal consists of a multi-purpose architecture that implement two broad modules in order to attend: (a) the range of processes related to the environment affectation, and (b) the range or processes related to the emotion perception-like and the higher levels of reasoning. This has required an intense and critical analysis beyond the state of the art around the most relevant theories of emotion and technical systems, in order to obtain the required support for those foundations that sustain each model. The problem has been interpreted and is described on the basis of AGSys, an agent assumed with the minimum rationality as to provide the capability to perform emotional assessment. AGSys is a conceptualization of a Model-based Cognitive agent that embodies an inner agent ESys, the responsible of performing the emotional operation inside of AGSys. The solution consists of multiple computational modules working federated, and aimed at conforming a mutual feedback loop between AGSys and ESys. Throughout this solution, the environment and the effects that might influence over the system are described as different problems. While AGSys operates as a common system within the external environment, ESys is designed to operate within a conceptualized inner environment. And this inner environment is built on the basis of those relevances that might occur inside of AGSys in the interaction with the external environment. This allows for a high-quality separate reasoning concerning mission goals defined in AGSys, and emotional goals defined in ESys. This way, it is provided a possible path for high-level reasoning under the influence of goals congruence. High-level reasoning model uses knowledge about emotional goals stability, letting this way new directions in which mission goals might be assessed under the situational state of this stability. This high-level reasoning is grounded by the work of MEP, a model of emotion perception that is thought as an analogy of a well-known theory in emotion science. The work of this model is described under the operation of a recursive-like process labeled as R-Loop, together with a system of emotional goals that are assumed as individual agents. This way, AGSys integrates knowledge that concerns the relation between a perceived object, and the effect which this perception induces on the situational state of the emotional goals. This knowledge enables a high-order system of information that provides the sustain for a high-level reasoning. The extent to which this reasoning might be approached is just delineated and assumed as future work. This thesis has been studied beyond a long range of fields of knowledge. This knowledge can be structured into two main objectives: (a) the fields of psychology, cognitive science, neurology and biological sciences in order to obtain understanding concerning the problem of the emotional phenomena, and (b) a large amount of computer science branches such as Autonomic Computing (AC), Self-adaptive software, Self-X systems, Model Integrated Computing (MIC) or the paradigm of models@runtime among others, in order to obtain knowledge about tools for designing each part of the solution. The final approach has been mainly performed on the basis of the entire acquired knowledge, and described under the fields of Artificial Intelligence, Model-Based Systems (MBS), and additional mathematical formalizations to provide punctual understanding in those cases that it has been required. This approach describes a reference model to feedback systems with valuable meaning, allowing for reasoning with regard to (a) the relationship between the environment and the relevance of the effects on the system, and (b) dynamical evaluations concerning the inner situational state of the system as a result of those effects. And this reasoning provides a framework of distinguishable states of AGSys derived from its own circumstances, that can be assumed as artificial emotion.
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Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved. Acknowledgements The author's studies in this field are supported by MRC grants G1002118 (NS and RAA) and G110357 (RAA), MR/L010011/1 (PAF), the European Community's Seventh Framework Programme (FP7/2007–2013) under grant agreement no. 212885 (PAF) and the Wellcome Trust (080388 to PAF). AS was funded by a BBSRC CASE Studentship co-funded by AstraZeneca.
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Funding: This project has received funding from the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement no. 613960 (SMARTBEES) (http://www.smartbees-fp7.eu/) and Veterinary Medicines Directorate, Department for Environment Food & Rural Affairs (Project # VM0517) (https://www.gov.uk/government/organisations/veterinary-medicines-directorate). CHM was supported by a Biosciences Knowledge Transfer Network Biotechnology and Biological Sciences Research Council (KTN-BBSRC CASE) Studentship (BB/L502467/1) (http://www.bbsrc.ac.uk/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Acknowledgments We gratefully acknowledge Mr Sebastian Bacz’s expert help and advice with beekeeping.
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Acknowledgements Mayuri Munasinghe was supported by a Commonwealth Scholarship (ref no. LKCS-2009-384). The development and use of the SNP chip was funded by a BBSRC grant BB/J003336/1. The authors thank Owen Price (University of Wollongong, Australia) for producing the coloured province map of Sri Lanka, Gareth Norton (Aberdeen) for merging the RDP1 SNP data with the Sri Lankan data and Tony Travis (Aberdeen) for help with PCA.
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We thank Donna Wallace and the animal house staff for their help with the animal studies. We thank Pat Bain for help in preparing the figures. This work was supported by the Biotechnology and Biological Science Research Council (BBSRC) grant number BB/K001043/1 (G.H., A.W.R., P.N.S., P.J.Mc. and P.J.M.) and the Scottish Government (A.W.R., L.M.T., C.D.M. and P.J.M.).
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Funded by OPTIMA Biotechnology & Biological Sciences Research Council (BBSRC) Institute Strategic Programme Energy Grasses & Biorefining. Grant Number: BBS/E/W/10963A01 Defra GIANT LINK