791 resultados para Concerns Based Adoption Model CBAM
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Objective of the thesis is to create a value based pricing model for marine engines and study the feasibility of implementing such model in the sales organization of a specific segment in the case company’s marine division. Different pricing strategies, concept of “value”, and how perceptions of value can be influenced through value based marketing are presented as theoretical background for the value based pricing model. Forbis and Mehta’s Economic Value to Customer (EVC) was selected as framework to create the value based pricing model for marine engines. The EVC model is based on calculating and comparing life-cycle costs of the reference product and competing products, thus showing the quantifiable value of the company’s own product compared to competition. In the applied part of the thesis, the components of the EVC model are identified for a marine diesel engine, the components are explained, and an example calculation created in Excel is presented. When examining the possibilities to implement in practice a value based pricing strategy based on the EVC model, it was found that the lack of precise information on competing products is the single biggest obstacle to use EVC exactly as presented in the literature. It was also found that sometimes necessary communication channels are missing and that there is simply a lack of interest from some clients and product end-users part to spend time on studying the life-cycle costs of the product. Information on the company’s own products is however sufficient and the sales force is capable to communicate to sufficiently high executive levels in the client organizations. Therefore it is suggested to focus on quantifying and communicating the company’s own value proposition. The dynamic nature of the business environment (variance in applications in which engines are installed, different clients, competition, end-clients etc.) means also that each project should be created its own EVC calculation. This is demanding in terms of resources needed, thus it is suggested to concentrate on selected projects and buyers, and to clients where the necessary communication channels to right levels in the customer organization are available. Finally, it should be highlighted that as literature suggests, implementing a value based pricing strategy is not possible unless the whole business approach is value based.
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The purpose of this study was to investigate the effect a human link through the One World Youth Project has on a global education program, if a human connection through the program enhances a student's ability to develop a critical consciousness of global issues, and the etTectiveness of thc constructivist-based Driver Model of Curriculum Development, which served as the curriculum model in this study. An action based research cycle was chosen as this study's research methodology and incorporated 5 qualitative data collection instruments: a) interviews and questionnaires, b) artifacts, c) teacher journal, d) critical friend's observation forms, and e) my critical friend's postobservation interviews. The data were conected from 4 student participants and my critical friend during all stages of the action research cycle. The results of this study provide educators with data on the impact of human connections in a global education program, the effects these connections have on students, and the effectiveness of the Driver Model of Curriculum Development. This study also provides practical activities and strategies that could be used by educators to develop their own global education programs. The United Nations drafted the Millennium Development Goals in an effort to improve the lives of billions of people across the globe. The eight goals were developed with the support of all member nations since all human beings are global citizens who have a responsibility to make the world a better place. Students need to develop a critical consciousness of global issues so that they can work with others to eliminate them. Students who are taught to restate the opinions of others win not be prepared to inherit a world full of challenges that will require new innovative ideas to foster positive change.
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Ce mémoire prend la forme d’une réflexion critique sur le modèle proposé par Hosler afin d’expliquer les taux quantifiés d’étain et d’arsénique dans des objets de statut métalliques Mésoaméricains provenant principalement de l’Occident mésoaméricain et couvrant les deux phases de développement de la métallurgie mésoaméricaine. Ces objets font partie de la collection du Museo Regional de Guadalajara. Plus particulièrement, ce mémoire s’intéresse aux grelots mésoaméricains puisqu’ils représentent un élément important de la métallurgie préhispanique en Mésoamérique. Cette réflexion critique soulève plusieurs considérations techniques, méthodologiques, étymologiques, iconographiques, ethnohistoriques et logiques du modèle de Hosler relativement à la couleur des alliages constituant les grelots mésoaméricains. Les paramètres sur lesquels Hosler base son modèle sont questionnables à plusieurs niveaux. Ainsi, le fait que les niveaux d’arsenic ou d’étain observés dans les alliages cupriques de biens utilitaires sont généralement inférieurs à ceux quantifiés dans les alliages cupriques usités pour la fabrication de biens de statut de la Période 2 pourrait s’expliquer par le fait qu’il s’agit de deux méthodes de fabrication distinctes ayant des contraintes techniques différentes ou que ces artéfacts ont des paramètres et des fonctions distinctes. Les limites de l’association soleil-or, lune-argent y sont également exposées et un chapitre est consacré à la sonorité.
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This thesis investigates a method for human-robot interaction (HRI) in order to uphold productivity of industrial robots like minimization of the shortest operation time, while ensuring human safety like collision avoidance. For solving such problems an online motion planning approach for robotic manipulators with HRI has been proposed. The approach is based on model predictive control (MPC) with embedded mixed integer programming. The planning strategies of the robotic manipulators mainly considered in the thesis are directly performed in the workspace for easy obstacle representation. The non-convex optimization problem is approximated by a mixed-integer program (MIP). It is further effectively reformulated such that the number of binary variables and the number of feasible integer solutions are drastically decreased. Safety-relevant regions, which are potentially occupied by the human operators, can be generated online by a proposed method based on hidden Markov models. In contrast to previous approaches, which derive predictions based on probability density functions in the form of single points, such as most likely or expected human positions, the proposed method computes safety-relevant subsets of the workspace as a region which is possibly occupied by the human at future instances of time. The method is further enhanced by combining reachability analysis to increase the prediction accuracy. These safety-relevant regions can subsequently serve as safety constraints when the motion is planned by optimization. This way one arrives at motion plans that are safe, i.e. plans that avoid collision with a probability not less than a predefined threshold. The developed methods have been successfully applied to a developed demonstrator, where an industrial robot works in the same space as a human operator. The task of the industrial robot is to drive its end-effector according to a nominal sequence of grippingmotion-releasing operations while no collision with a human arm occurs.
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Una preocupació constant per als investigadors dels diferents camps relacionats amb l’educació obligatòria, així com també de les administracions educatives i d’altres organismes que hi incideixen, consisteix a seleccionar continguts i orientar els processos més útils per aconseguir que els nous ciutadans s’integrin en la societat avançada del segle XXI. Es vol que s’integrin amb unes eines culturals mínimes i que adquireixen la capacitat d’autoaprenentatge, considerada com a bàsica per entendre la complexitat i inserir-se en el món actual
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We describe a general likelihood-based 'mixture model' for inferring phylogenetic trees from gene-sequence or other character-state data. The model accommodates cases in which different sites in the alignment evolve in qualitatively distinct ways, but does not require prior knowledge of these patterns or partitioning of the data. We call this qualitative variability in the pattern of evolution across sites "pattern-heterogeneity" to distinguish it from both a homogenous process of evolution and from one characterized principally by differences in rates of evolution. We present studies to show that the model correctly retrieves the signals of pattern-heterogeneity from simulated gene-sequence data, and we apply the method to protein-coding genes and to a ribosomal 12S data set. The mixture model outperforms conventional partitioning in both these data sets. We implement the mixture model such that it can simultaneously detect rate- and pattern-heterogeneity. The model simplifies to a homogeneous model or a rate- variability model as special cases, and therefore always performs at least as well as these two approaches, and often considerably improves upon them. We make the model available within a Bayesian Markov-chain Monte Carlo framework for phylogenetic inference, as an easy-to-use computer program.
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New construction algorithms for radial basis function (RBF) network modelling are introduced based on the A-optimality and D-optimality experimental design criteria respectively. We utilize new cost functions, based on experimental design criteria, for model selection that simultaneously optimizes model approximation, parameter variance (A-optimality) or model robustness (D-optimality). The proposed approaches are based on the forward orthogonal least-squares (OLS) algorithm, such that the new A-optimality- and D-optimality-based cost functions are constructed on the basis of an orthogonalization process that gains computational advantages and hence maintains the inherent computational efficiency associated with the conventional forward OLS approach. The proposed approach enhances the very popular forward OLS-algorithm-based RBF model construction method since the resultant RBF models are constructed in a manner that the system dynamics approximation capability, model adequacy and robustness are optimized simultaneously. The numerical examples provided show significant improvement based on the D-optimality design criterion, demonstrating that there is significant room for improvement in modelling via the popular RBF neural network.
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A fundamental principle in practical nonlinear data modeling is the parsimonious principle of constructing the minimal model that explains the training data well. Leave-one-out (LOO) cross validation is often used to estimate generalization errors by choosing amongst different network architectures (M. Stone, "Cross validatory choice and assessment of statistical predictions", J. R. Stast. Soc., Ser. B, 36, pp. 117-147, 1974). Based upon the minimization of LOO criteria of either the mean squares of LOO errors or the LOO misclassification rate respectively, we present two backward elimination algorithms as model post-processing procedures for regression and classification problems. The proposed backward elimination procedures exploit an orthogonalization procedure to enable the orthogonality between the subspace as spanned by the pruned model and the deleted regressor. Subsequently, it is shown that the LOO criteria used in both algorithms can be calculated via some analytic recursive formula, as derived in this contribution, without actually splitting the estimation data set so as to reduce computational expense. Compared to most other model construction methods, the proposed algorithms are advantageous in several aspects; (i) There are no tuning parameters to be optimized through an extra validation data set; (ii) The procedure is fully automatic without an additional stopping criteria; and (iii) The model structure selection is directly based on model generalization performance. The illustrative examples on regression and classification are used to demonstrate that the proposed algorithms are viable post-processing methods to prune a model to gain extra sparsity and improved generalization.
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We compared output from 3 dynamic process-based models (DMs: ECOSSE, MILLENNIA and the Durham Carbon Model) and 9 bioclimatic envelope models (BCEMs; including BBOG ensemble and PEATSTASH) ranging from simple threshold to semi-process-based models. Model simulations were run at 4 British peatland sites using historical climate data and climate projections under a medium (A1B) emissions scenario from the 11-RCM (regional climate model) ensemble underpinning UKCP09. The models showed that blanket peatlands are vulnerable to projected climate change; however, predictions varied between models as well as between sites. All BCEMs predicted a shift from presence to absence of a climate associated with blanket peat, where the sites with the lowest total annual precipitation were closest to the presence/absence threshold. DMs showed a more variable response. ECOSSE predicted a decline in net C sink and shift to net C source by the end of this century. The Durham Carbon Model predicted a smaller decline in the net C sink strength, but no shift to net C source. MILLENNIA predicted a slight overall increase in the net C sink. In contrast to the BCEM projections, the DMs predicted that the sites with coolest temperatures and greatest total annual precipitation showed the largest change in carbon sinks. In this model inter-comparison, the greatest variation in model output in response to climate change projections was not between the BCEMs and DMs but between the DMs themselves, because of different approaches to modelling soil organic matter pools and decomposition amongst other processes. The difference in the sign of the response has major implications for future climate feedbacks, climate policy and peatland management. Enhanced data collection, in particular monitoring peatland response to current change, would significantly improve model development and projections of future change.
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Abstract: Following a workshop exercise, two models, an individual-based landscape model (IBLM) and a non-spatial life-history model were used to assess the impact of a fictitious insecticide on populations of skylarks in the UK. The chosen population endpoints were abundance, population growth rate, and the chances of population persistence. Both models used the same life-history descriptors and toxicity profiles as the basis for their parameter inputs. The models differed in that exposure was a pre-determined parameter in the life-history model, but an emergent property of the IBLM, and the IBLM required a landscape structure as an input. The model outputs were qualitatively similar between the two models. Under conditions dominated by winter wheat, both models predicted a population decline that was worsened by the use of the insecticide. Under broader habitat conditions, population declines were only predicted for the scenarios where the insecticide was added. Inputs to the models are very different, with the IBLM requiring a large volume of data in order to achieve the flexibility of being able to integrate a range of environmental and behavioural factors. The life-history model has very few explicit data inputs, but some of these relied on extensive prior modelling needing additional data as described in Roelofs et al.(2005, this volume). Both models have strengths and weaknesses; hence the ideal approach is that of combining the use of both simple and comprehensive modeling tools.
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Bin planning (arrangements) is a key factor in the timber industry. Improper planning of the storage bins may lead to inefficient transportation of resources, which threaten the overall efficiency and thereby limit the profit margins of sawmills. To address this challenge, a simulation model has been developed. However, as numerous alternatives are available for arranging bins, simulating all possibilities will take an enormous amount of time and it is computationally infeasible. A discrete-event simulation model incorporating meta-heuristic algorithms has therefore been investigated in this study. Preliminary investigations indicate that the results achieved by GA based simulation model are promising and better than the other meta-heuristic algorithm. Further, a sensitivity analysis has been done on the GA based optimal arrangement which contributes to gaining insights and knowledge about the real system that ultimately leads to improved and enhanced efficiency in sawmill yards. It is expected that the results achieved in the work will support timber industries in making optimal decisions with respect to arrangement of storage bins in a sawmill yard.
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The open provenance architecture (OPA) approach to the challenge was distinct in several regards. In particular, it is based on an open, well-defined data model and architecture, allowing different components of the challenge workflow to independently record documentation, and for the workflow to be executed in any environment. Another noticeable feature is that we distinguish between the data recorded about what has occurred, emphprocess documentation, and the emphprovenance of a data item, which is all that caused the data item to be as it is and is obtained as the result of a query over process documentation. This distinction allows us to tailor the system to separately best address the requirements of recording and querying documentation. Other notable features include the explicit recording of causal relationships between both events and data items, an interaction-based world model, intensional definition of data items in queries rather than relying on explicit naming mechanisms, and emphstyling of documentation to support non-functional application requirements such as reducing storage costs or ensuring privacy of data. In this paper we describe how each of these features aid us in answering the challenge provenance queries.
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Due to the increase in water demand and hydropower energy, it is getting more important to operate hydraulic structures in an efficient manner while sustaining multiple demands. Especially, companies, governmental agencies, consultant offices require effective, practical integrated tools and decision support frameworks to operate reservoirs, cascades of run-of-river plants and related elements such as canals by merging hydrological and reservoir simulation/optimization models with various numerical weather predictions, radar and satellite data. The model performance is highly related with the streamflow forecast, related uncertainty and its consideration in the decision making. While deterministic weather predictions and its corresponding streamflow forecasts directly restrict the manager to single deterministic trajectories, probabilistic forecasts can be a key solution by including uncertainty in flow forecast scenarios for dam operation. The objective of this study is to compare deterministic and probabilistic streamflow forecasts on an earlier developed basin/reservoir model for short term reservoir management. The study is applied to the Yuvacık Reservoir and its upstream basin which is the main water supply of Kocaeli City located in the northwestern part of Turkey. The reservoir represents a typical example by its limited capacity, downstream channel restrictions and high snowmelt potential. Mesoscale Model 5 and Ensemble Prediction System data are used as a main input and the flow forecasts are done for 2012 year using HEC-HMS. Hydrometeorological rule-based reservoir simulation model is accomplished with HEC-ResSim and integrated with forecasts. Since EPS based hydrological model produce a large number of equal probable scenarios, it will indicate how uncertainty spreads in the future. Thus, it will provide risk ranges in terms of spillway discharges and reservoir level for operator when it is compared with deterministic approach. The framework is fully data driven, applicable, useful to the profession and the knowledge can be transferred to other similar reservoir systems.
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Desde 2013, a Prefeitura do Município de Osasco iniciou a implantação de um modelo de gestão por resultados, com a utilização da metodologia do Balanced Scorecard (BSC) e a celebração de Acordos de Resultados entre o prefeito e respectivos secretários. A dissertação objetivou, inicialmente, responder à demanda da Secretaria de Planejamento e Gestão (Seplag), de realizar diagnóstico e propor soluções de incentivos prioritariamente não financeiros para estimular os funcionários a perseguirem o cumprimento dos objetivos e metas contidos no planejamento estratégico da prefeitura. Neste sentido, buscou-se estabelecer um modelo conceitual para embasar a adoção de políticas de reconhecimento no trabalho, especialmente no âmbito da gestão pública. Adicionalmente, verificou-se a necessidade de agregar dois novos objetivos àquele proposto de início: (1) avaliar o nível de disseminação e desdobramento até as equipes de ponta das metas estratégicas pactuadas nos Acordos de Resultados, utilizando-se como referência a metodologia do BSC; (2) verificar o nível de aderência do modelo de gestão atualmente praticado pela prefeitura de Osasco aos preceitos da chamada Nova Gestão Pública (NGP). Ao final, são propostas ações para o aperfeiçoamento e continuidade do modelo de gestão adotado.
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Esta dissertação estuda a propagação de crises sobre o sistema financeiro. Mais especi- ficamente, busca-se desenvolver modelos que permitam simular como um determinado choque econômico atinge determinados agentes do sistema financeiro e apartir dele se propagam, transformando-se em um problema sistêmico. A dissertação é dividida em dois capítulos,além da introdução. O primeiro capítulo desenvolve um modelo de propa- gação de crises em fundos de investimento baseado em ciência das redes.Combinando dois modelos de propagação em redes financeiras, um simulando a propagação de perdas em redes bipartites de ativos e agentes financeiros e o outro simulando a propagação de perdas em uma rede de investimentos diretos em quotas de outros agentes, desenvolve-se um algoritmo para simular a propagação de perdas através de ambos os mecanismos e utiliza-se este algoritmo para simular uma crise no mercado brasileiro de fundos de investimento. No capítulo 2,desenvolve-se um modelo de simulação baseado em agentes, com agentes financeiros, para simular propagação de um choque que afeta o mercado de operações compromissadas.Criamos também um mercado artificial composto por bancos, hedge funds e fundos de curto prazo e simulamos a propagação de um choque de liquidez sobre um ativo de risco securitizando utilizado para colateralizar operações compromissadas dos bancos.