This study is about the nature of persons and personal identity. It belongs to a tradition that maintains that in order to understand what it is to be a person we must clarify what personal identity consists in. In this pursuit, I differentiate between the problems (i) How do persons persist? and (ii) What facts, if any, does personal identity consist in? Concerning the first question, I argue that persons persist three-dimensionally (the endurance view), and not four-dimensionally (the perdurarne view), on the ground that objects must always fall under some substance sortal concept S (the sortal dependency of individuation), and that the concept person entails that objects falling under it are three-dimensional. Concerning the second question, I differentiate between Criterianists, who maintain that it is possible to specify a non-circular and informative criterion for personal identity, and Non-Criterianists, who deny that such a specification is possible. I argue against Criterianist accounts of personal identity on the ground that they are either (i) circular, (ii) violate the intrinsicality of identity or (iii) do not adequately represent what we are essentially. I further criticise three Psychological Non-Criterianist accounts of personal identity on the ground that they wrongly assume that 'person' refers to mental entities. Instead I formulate the Revised Animal Attribute View where person is understood as a basic sortal concept which picks out a biological sort of enduring animals. In this, I claim that the real essence of a person is determined by the real essence of the kind of animal he is, without thereby denying that persons have a real essence as persons. / digitalisering@umu

Of key importance for metamodelling are appropriate modelling formalisms. Most metamodelling languages permit the development of metamodels that specify tree-structured models enriched with semantics like constraints, references and operations, which extend the models to graphs. However, often the semantics of these semantic constructs is not part of the metamodel, i.e., it is unspeci ed. Therefore, we propose to reuse well-known compiler construction techniques to specify metamodel semantics. To be more precise, we present the application of reference attribute grammars (RAGs) for metamodel semantics and analyse commonalities and differences. Our focus is to pave the way for such a combination, by exemplifying why and how the metamodelling and attribute grammar (AG) world can be combined and by investigating a concrete example - the combination of the Eclipse Modelling Framework (EMF) and JastAdd, an AG evaluator generator.

This study integrates sequence stratigraphy of the Late Permian Toolachee Formation in the non-marine intracratonic Permian-Triassic Cooper Basin, Australia with 3-D seismic attribute analysis to predict the extent of depositional environments identified on wireline and well core data. The low resolution seismic data (tuning thickness 23 - 31 m) comprised of six seismic horizons allowed the successful testing of sequence stratigraphic interpretations of the productive Toolachee Formation that were based on wireline data. The analysis of 29 well logs and three 20 m core intervals resulted in the identification of eleven parasequences that comprise the building blocks of an overall transitional systems tract, characterised by a gradual increase in accommodation. The parasequences reflect cyclic transitions between braided and meandering fluvial systems as a result of fluctuations in sediment flux, possibly driven by Milankovitch climatic-forcing. The seismic horizon attribute maps image mostly the meandering fluvial bodies within the upper parts of the parasequences, but some maps image the lower amalgamated sand sheets and show no channel structures. Categorisation of the fluvial bodies in the overbank successions reflects a gradual decrease in sinuosity, channel width, and channel belt width up-section, supporting the overall increase in accommodation up-section. Similar acoustic impedance values for shales and sands do not suggest successful seismic forward modelling between the two lithologies. Geological interpretations suggest most imaged channel fill to be made up predominantly of fine sediments, as channel avulsion and abandonment is common and increases with time. Seismic forward modelling resulted in the interpretation of carbonaceous shale as a possible channel fill, supporting the geological interpretations. The three major identified fluvial styles; braided, meanders, and distributaries are potential targets for future exploration. Extensive sand sheets deposited from braided fluvial systems require structural traps for closure. Meandering and anastomosing channel systems represent excellent stratigraphic traps, such as the basal sands/gravels of laterally accreted point bars.

Proposed in this thesis is a methodology to enable yacht designers to develop innovative structural concepts, even when the loads experienced by the yacht are highly uncertain, and has been implemented in sufficient detail to confirm the feasibility of this new approach. The new approach is required because today??s yachts are generally lighter, getting larger and going faster. The question arises as to how far the design envelope can be pushed with the highly uncertain loads experienced by the structure? What are the effects of this uncertainty and what trade-offs in the structural design will best meet the overall design objectives? The new approach provides yacht designers with a means of developing innovative structural solutions that accommodate high levels of uncertainty, but still focus on best meeting design objectives constrained by trade-offs in weight, safety and cost. The designer??s preferences have a large, and not always intuitive, influence on the necessary design trade-offs. This in turn invites research into ways to formally integrate decision algorithms into knowledge-based design systems. A lean and robust design system has been achieved by developing a set of tools which are blanketed by a fuzzy decision algorithm. The underlying tool set includes costing, material optimisation and safety analysis. Central to this is the innovative way in which the system allows non-discrete variables to be utilized along with new subjective measures of structural reliability based on load path algorithms and topological (shape) optimisation. The originality in this work is the development of a knowledge-based framework and methodology that uses a fuzzy decision making tool to navigate through a design space and address trade-offs between high level objectives when faced with limited design detail and uncertainty. In so doing, this work introduces the use of topological optimisation and load path theory to the structural design of yachts as a means of overcoming the historical focus of knowledge-based systems and to ensure that innovative solutions can still evolve. A sensitivity analysis is also presented which can quantify a design??s robustness in a system that focuses on a global approach to the measurement of objectives such as cost, weight and safety. Results from the application of this system show new and innovative structural solutions evolving that take into account the designers preferences regarding cost, weight and safety while accommodating uncertain parameters such as the loading experienced by the hull.

The use of seismic methods in hard rock environments in Western Australia for mineral exploration is a new and burgeoning technology. Traditionally, mineral exploration has relied upon potential field methods and surface prospecting to reveal shallow targets for economic exploitation. These methods have been and will continue to be effective but lack lateral and depth resolution needed to image deeper mineral deposits for targeted mining. With global need for minerals, and gold in particular, increasing in demand, and with shallower targets harder to find, new methods to uncover deeper mineral reserves are needed. Seismic reflection imaging, hard rock borehole data analysis, seismic inversion and seismic attribute analysis all give the spatial and volumetric exploration techniques the mineral industry can use to reveal high value deeper mineral targets. / In 2002, two high resolution seismic lines, the East Victory and Intrepid, were acquired along with sonic logging, to assess the feasibility of seismic imaging and rock characterisation at the St. Ives gold camp in Western Australia. An innovative research project was undertaken combining seismic processing, rock characterization, reflection calibration, seismic inversion and seismic attribute analysis to show that volumetric predictions of rock type and gold-content may be viable in hard rock environments. Accurate seismic imaging and reflection identification proved to be challenging but achievable task in the all-out hard rock environment of the Yilgarn craton. Accurate results were confounded by crocked seismic line acquisition, low signal-to-noise ratio, regolith distortions, small elastic property variations in the rock, and a limited volume of sonic logging. Each of these challenges, however, did have a systematic solution which allowed for accurate results to be achieved. / Seismic imaging was successfully completed on both the East Victory and Intrepid data sets revealing complex structures in the Earth as shallow as 100 metres to as deep as 3000 metres. The successful imaging required homogenization of the regolith to eliminate regolith travel-time distortions and accurate constant velocity analysis for reflection focusing using migration. Verification of the high amplitude reflections within each image was achieved through integration of surface geological and underground mine data as well as calibration with log derived synthetic seismograms. The most accurate imaging results were ultimately achieved on the East Victory line which had good signal-to-noise ratio and close-to-straight data acquisition direction compared to the more crooked Intrepid seismic line. / The sonic logs from both the East Victory and Intrepid seismic lines were comprehensively analysed by re-sampling and separating the data based on rock type, structure type, alteration type, and Au assay. Cross plotting of the log data revealed statistically accurate separation between harder and softer rocks, as well as sheared and un-sheared rock, were possible based solely on compressional-wave, shear-wave, density, acoustic and elastic impedance. These results were used successfully to derive empirical relationships between seismic attributes and geology. Calibrations of the logs and seismic data provided proof that reflections, especially high-amplitude reflections, correlated well with certain rock properties as expected from the sonic data, including high gold content sheared zones. The correlation value, however, varied with signal-to-noise ratio and crookedness of the seismic line. Subsequent numerical modelling confirmed that separating soft from hard rocks can be based on both general reflectivity pattern and impedance contrasts. / Indeed impedance inversions on the calibrated seismic and sonic data produced reliable volumetric separations between harder rocks (basalt and dolerite) and softer rock (intermediate intrusive, mafic, and volcaniclastic). Acoustic impedance inversions produced the most statistically valid volumetric predictions with the simultaneous use of acoustic and elastic inversions producing stable separation of softer and harder rocks zones. Similarly, Lambda-Mu-Rho inversions showed good separations between softer and harder rock zones. With high gold content rock associated more with “softer” hard rocks and sheared zones, these volumetric inversion provide valuable information for targeted mining. The geostatistical method applied to attribute analysis, however, was highly ambiguous due to low correlations and thus produced overly generalized predictions. Overall reliability of the seismic inversion results were based on quality and quantity of sonic data leaving the East Victory data set, again with superior results as compared to the Intrepid data set. / In general, detailed processing and analysis of the 2D seismic data and the study of the relationship between the recorded wave-field and rock properties measured from borehole logs, core samples and open cut mining, revealed that positive correlations can be developed between the two. The results of rigorous research show that rock characterization using seismic methodology will greatly benefit the mineral industry.

[Truncated abstract] In this thesis, we introduce a mammogram analysis system developed for the automatic segmentation and analysis of mammograms. This original system has been designed to aid radiologists to detect breast cancer on mammograms. The system embodies attribute-driven segmentation in which the attributes of an image are extracted progressively in a step-by-step, hierarchical fashion. Global, low-level attributes obtained in the early stages are used to derive local, high-level attributes in later stages, leading to increasing refinement and accuracy in image segmentation and analysis. The proposed system can be characterized as: • a bootstrap engine driven by the attributes of the images; • a solid framework supporting the process of hierarchical segmentation; • a universal platform for the development and integration of segmentation and analysis techniques; and • an extensible database in which knowledge about the image is accumulated. Central to this system are three major components: 1. a series of applications for attribute acquisition; 2. a standard format for attribute normalization; and 3. a database for attribute storage and data exchange between applications. The first step of the automatic process is to segment the mammogram hierarchically into several distinctive regions that represent the anatomy of the breast. The adequacy and quality of the mammogram are then assessed using the anatomical features obtained from segmentation. Further image analysis, such as breast density classification and lesion detection, may then be carried out inside the breast region. Several domain-specific algorithms have therefore been developed for the attribute acquisition component in the system. These include: 1. automatic pectoral muscle segmentation; 2. adequacy assessment of positioning and exposure; and 3. contrast enhancement of mass lesions. An adaptive algorithm is described for automatic segmentation of the pectoral muscle on mammograms of mediolateral oblique (MLO) views

A automação da classificação visual de produtos vem ganhando cada vez mais importância nos processos produtivos agrícolas. Isto posto, uma das principais dificuldades encontradas por produtores de flores e plantas ornamentais é garantir o crescimento homogêneo de suas plantas. Nesse cenário, as mudas utilizadas para gerar as plantas são importantes uma vez que se pode estimar seu potencial de crescimento através de uma inspeção visual. Sendo assim, um sistema de visão computacional pode ser empregado para automatizar essa tarefa. Porém, diferentemente de indústrias tradicionais, a indústria agrícola apresenta grande variabilidade entre os produtos analisados. Técnicas de aprendizado de máquina supervisionado conseguem avaliar um conjunto de atributos referentes ao objeto inspecionado para classificá-lo corretamente, de forma a lidar tanto com a variabilidade dos produtos em inspeção quanto com a incorporação do conhecimento de especialistas pelo sistema. A definição do conjunto de atributos a ser extraído das imagens dos produtos é de extrema importância, pois é ele quem fornece toda a informação utilizada no sistema. Um conjunto com diversos atributos assegura que toda a informação necessária é capturada, porém atributos irrelevantes ou redundantes podem prejudicar o desempenho dos classificadores. Técnicas de seleção de atributos podem ser utilizadas para equilibrar essas necessidades. O objetivo do trabalho foi o de avaliar a eficácia dessas técnicas para a classificação de mudas de violeta. Vinte e seis parâmetros foram extraídos de seiscentas imagens rotuladas em quatro níveis de qualidade. Em seguida, os desempenhos de seis classificadores foram comparados considerando-se um universo de subconjuntos gerados por quatro técnicas de seleção de atributos. Os resultados mostraram que essas técnicas são realmente vantajosas, gerando ganhos de até 8,8% nas taxas de acertos e ao mesmo tempo reduzindo de 26 para 11 o número médio de atributos utilizados. O classificador Logistic Regression associado ao subconjunto gerado pelo Chi-quadrado foi o que apresentou melhor desempenho global, atingindo 80% de acerto. O classificador Random Forest ficou em segundo lugar, porém se mostrou menos sensível a seleção de atributos. / The automation of visual classification of products is gaining more importance in agricultural production processes. That said, one of the main difficulties encountered by ornamental plants and flowers producers is to ensure homogeneous growth of their plants. In this scenario, the seedlings used to grow the plants are very important since it is possible to estimate their growth potential by means of a visual inspection. Therefore, a computer vision system can be used to automate this task. Unlike traditional industries, the agricultural industry shows great variability among the products inspected. Supervised machine learning techniques can evaluate an attribute set representing the inspected object in order to correctly classify it, making it possible not only to deal with the variability of the inspected products but also with the incorporation of experts knowledge into the system. The definition of the attribute set to be extracted from the images of the products is of utmost importance, as it is it that provides all information used by the system. A set with several attributes ensures that all necessary information is captured; however irrelevant or redundant attributes can affect the performance of classifiers. Attribute selection techniques can be used to balance these needs. The aim of this study was then to evaluate the effectiveness of these techniques regarding the classification of African violet seedlings. Twenty- six parameters were extracted from six hundred images, labeled into four quality groups. Then, the performances of six classifiers were compared by considering the universe of subsets generated by four attribute selection techniques. The results showed that these techniques are indeed advantageous, generating gains of up to 8.8% in accuracy rate while reducing from 26 to 11 the average number of attributes used. Logistic Regression classifier, associated with the subset generated by the Chi-squared filter showed the best overall performance, achieving 80 % accuracy. Random Forest was second, but was less sensitive to attribute selection.

This study uses a simulation to gather data regarding a payment disruption. The simulation is part of a project called CCRAAAFFFTING, which examines what happens to a society when a payment disruption occurs. The purpose of this study is to develop a measure for resilience in the simulation and use machine learning to analyse the attributes in the simulation to see how they affect the resilience in the society. The resilience is defined as “the ability to bounce back to a previous state”, and the resilience measure is developed according to this definition. Two resilience measurements are defined, one which relates the simulated value to the best-case and worst-case scenarios, and the other which takes the pace of change in values into consideration. These two measurements are then combined to one measure of the total resilience. The three machine learning algorithms compared are Neural Network, Support Vector Machine and Random Forest, and the performance measure of these are the error rate. The results show that Random Forest performs significantly better than the other two algorithms, and that the most important attributes in the simulation are those concerning the customers’ ability to make purchases in the simulation. The developed resilience measure proves to respond logically to how the situation unfolded, and some suggestions to further improve the measurement is provided for future research. / I denna studie används en simulering för att samla in data. Simuleringen är en del i ett projekt som kallas för CCRAAAFFFTING, vars syfte är att undersöka vad som händer i ett samhälle om en störning i betalsystemet inträffar. Syftet med denna studie är att utveckla ett mått för resiliens i simuleringen, samt att använda machine learning för att analysera attributen i simuleringen för att se hur de påverkar resiliensen i samhället. Resiliensen definieras enligt ”förmågan att snabbt gå tillbaka till ett tidigare stadie”, och resiliensmåttet utvecklas i enlighet med denna definition. Två resiliensmått definieras, där det ena måttet relaterar det simulerade värdet till de värsta och bästa scenarierna, och det andra måttet tar i beaktning hur snabbt värdena förändrades. Dessa två mått kombineras sedan till ett mått för den totala resiliensen. De tre machine learning-algoritmerna som jämförs är Neural Network, Support Vector Machine och Random Forest, och måttet för hur de presterar är felfrekvens. Resultaten visar att Random Forest presterar märkbart bättre än de andra två algoritmerna, och att de viktigaste attributen i simuleringen är de som berör kunders möjlighet att genomföra köp i simuleringen. Det utvecklade resiliensmåttet svarar på ett logiskt sätt enligt hur situationen utvecklar sig, och några förslag för att vidare utveckla måttet ges för vidare forskning.

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SILVA, Yuri Lopes. O componente animal em agroecossistemas trabalhados pelo Projeto Sustentare na comunidade Sítio Areias, Sobral - CE. 2015. 124 f. Dissertação (Mestrado em Desenvolvimento e Meio Ambiente) - Universidade Federal do Ceará, Fortaleza, 2015. / Submitted by Eric Santiago (erichhcl@gmail.com) on 2016-08-24T13:17:07Z No. of bitstreams: 1 2015_dis_ylsilva.pdf: 1823683 bytes, checksum: b9d37643d9146d817a1d3382c8f761c7 (MD5) / Approved for entry into archive by Jairo Viana (jairo@ufc.br) on 2016-08-26T18:39:39Z (GMT) No. of bitstreams: 1 2015_dis_ylsilva.pdf: 1823683 bytes, checksum: b9d37643d9146d817a1d3382c8f761c7 (MD5) / Made available in DSpace on 2016-08-26T18:39:39Z (GMT). No. of bitstreams: 1 2015_dis_ylsilva.pdf: 1823683 bytes, checksum: b9d37643d9146d817a1d3382c8f761c7 (MD5) Previous issue date: 2015 / Sustainability of livestock farming in family units covers agroecological knowledge capable of overcoming the harmful effects of inadequate management. Also, it promotes agricultural biodiversity, food security and the acquisition of income. In this perspective, the Sustentare Project proposes the rise of a conservative agriculture, encouraging the active participation of stakeholders in decision-making. The objectives were to understand the importance of livestock for the families participating in the Sustentare Project at Sitio Areias community; prepare a document on the overview of agroecosystems that include the activity and evaluate the importance of the livestock subsystem for balance and functioning of agroecosystems. The survey had a social character with a systems approach and relied on the Participatory Rural Appraisal tools, plus a semi-structured interview; an ethnobotanical survey to characterize the Collective Yard; an economic assessment and analysis of sustainability through systemic attributes. Family farms in Sitio Areias essentially occupy smallholdings of 0.2 to 16.0 ha. Livestock exhibits an apparent relationship between the available area and managed diversity of animal species. The product of this diversity of livestock appears with three hierarchically structured purposes, starting with the consumption, followed by marketing and donating or sharing. Cattle’s rising is no longer a viable option among the peasants of Sitio Areias. The poultry industry is more widespread among the families and its main purpose is to meet the self-consumption, but has a high potential of monetary income generation distributed throughout the year. The pig husbandry has demonstrated its importance in maintaining the financial position of the family dedicated to this activity, but needs to overcome difficulties such as poor sanitary management. Goat breeding has evolved in the community with the arrival of the Cabra Nossa de Cada Dia Project but does not meet any more the social role of providing daily milk to children's development. At Sitio Areias the Sustentare Project has been innovating with a strategy that seeks to communicate and to integrate, in a systemic way, several agroecosystems that usually are handled independently by different households. Thus, it challenges the approach centered in family agroecosystems and places the question of collective work, beyond the ideological perspective. / A sustentabilidade da produção animal em unidades familiares abrange conhecimentos agroecológicos capazes de superar os efeitos danosos de um manejo inadequado. Além disso, favorece a agrobiodiversidade, a segurança alimentar e a aquisição de renda, incluindo a atividade no processo de organização sócio econômica. Nessa perspectiva, o Projeto Sustentare propõe a ascensão de uma agricultura conservadora, estimulando a participação ativa dos atores sociais nas tomadas de decisões. Os objetivos do trabalho foram compreender a importância do componente animal para as famílias participantes do Projeto Sustentare na comunidade Sítio Areias; elaborar um documento sobre o panorama geral dos agroecossistemas que contemplam a atividade e avaliar a importância do subsistema de criação para o equilíbrio e funcionamento dos ecossistemas agrícolas. A pesquisa teve caráter social com uma abordagem sistêmica e contou com ferramentas do Diagnóstico Rural Participativo, além de uma entrevista semiestruturada; de um levantamento etnobotânico para a caracterização do Quintal Coletivo; de uma avaliação econômica e da análise da sustentabilidade por meio de atributos sistêmicos. A agricultura familiar no Sítio Areias ocupa essencialmente minifúndios de 0,2 a 16,0 ha. A criação exibe uma aparente relação entre a área disponível e a diversidade de espécies animais manejadas. O produto dessa diversidade de criações se apresenta com três finalidades hierarquicamente estruturadas, iniciando com o consumo, seguido da comercialização e da doação ou partilha. A bovinocultura, não é mais uma opção viável entre os camponeses do Sítio Areias. A avicultura está mais generalizada entre as famílias, tendo como principal propósito atender o autoconsumo, mas apresenta elevado potencial de geração de renda monetária distribuída ao longo do ano. A suinocultura tem demonstrado a sua relevância na manutenção da situação financeira da família dedicada a essa atividade, mas precisa superar dificuldades como o precário manejo sanitário. A caprinocultura evoluiu na comunidade com a chegada do Projeto Cabra Nossa de Cada Dia, mas não cumpre mais o papel social de fornecer o leite diário para o desenvolvimento infantil. No Sitio Areias, o Projeto Sustentare vem inovando com uma estratégia que busca comunicar e até integrar, de forma sistêmica, diferentes agroecossistemas que, usualmente, são manejados de forma independente por diferentes núcleos familiares. Assim, desafia a abordagem centrada nos agroecossistemas familiares e recoloca a questão do trabalho coletivo, para além da perspectiva ideológica.