Calculating the application criticality and business risk from technology obsolescence

The problem of technology obsolescence in information intensive businesses (software and hardware no longer being supported and replaced by improved and different solutions) and a cost constrained market can severely increase costs and operational, and ultimately reputation risk. Although many businesses recognise technological obsolescence, the pervasive nature of technology often means they have little information to identify the risk and location of pending obsolescence and little money to apply to the solution. This paper presents a low cost structured method to identify obsolete software and the risk of their obsolescence where the structure of a business and its supporting IT resources can be captured, modelled, analysed and the risk to the business of technology obsolescence identified to enable remedial action using qualified obsolescence information. The technique is based on a structured modelling approach using enterprise architecture models and a heatmap algorithm to highlight high risk obsolescent elements. The method has been tested and applied in practice in three consulting studies carried out by Capgemini involving four UK police forces. However the generic technique could be applied to any industry based on plans to improve it using ontology framework methods. This paper contains details of enterprise architecture meta-models and related modelling.

Methane in the atmosphere of the transiting hot Neptune GJ436b?

We present an analysis of seven primary transit observations of the hot Neptune GJ436b at 3.6, 4.5, and 8 μm obtained with the Infrared Array Camera on the Spitzer Space Telescope. After correcting for systematic effects, we fitted the light curves using the Markov Chain Monte Carlo technique. Combining these new data with the EPOXI, Hubble Space Telescope, and ground-based V, I, H, and Ks published observations, the range 0.5-10 μm can be covered. Due to the low level of activity of GJ436, the effect of starspots on the combination of transits at different epochs is negligible at the accuracy of the data set. Representative climate models were calculated by using a three-dimensional, pseudospectral general circulation model with idealized thermal forcing. Simulated transit spectra of GJ436b were generated using line-by-line radiative transfer models including the opacities of the molecular species expected to be present in such a planetary atmosphere. A new, ab-initio-calculated, line list for hot ammonia has been used for the first time. The photometric data observed at multiple wavelengths can be interpreted with methane being the dominant absorption after molecular hydrogen, possibly with minor contributions from ammonia, water, and other molecules. No clear evidence of carbon monoxide and carbon dioxide is found from transit photometry. We discuss this result in the light of a recent paper where photochemical disequilibrium is hypothesized to interpret secondary transit photometric data. We show that the emission photometric data are not incompatible with the presence of abundant methane, but further spectroscopic data are desirable to confirm this scenario.

Using palaeo-climate comparisons to constrain future projections in CMIP5

We present a selection of methodologies for using the palaeo-climate model component of the Coupled Model Intercomparison Project (Phase 5) (CMIP5) to attempt to constrain future climate projections using the same models. The constraints arise from measures of skill in hindcasting palaeo-climate changes from the present over three periods: the Last Glacial Maximum (LGM) (21 000 yr before present, ka), the mid-Holocene (MH) (6 ka) and the Last Millennium (LM) (850–1850 CE). The skill measures may be used to validate robust patterns of climate change across scenarios or to distinguish between models that have differing outcomes in future scenarios. We find that the multi-model ensemble of palaeo-simulations is adequate for addressing at least some of these issues. For example, selected benchmarks for the LGM and MH are correlated to the rank of future projections of precipitation/temperature or sea ice extent to indicate that models that produce the best agreement with palaeo-climate information give demonstrably different future results than the rest of the models. We also explore cases where comparisons are strongly dependent on uncertain forcing time series or show important non-stationarity, making direct inferences for the future problematic. Overall, we demonstrate that there is a strong potential for the palaeo-climate simulations to help inform the future projections and urge all the modelling groups to complete this subset of the CMIP5 runs.

Road and Traffic Signs Recognition using Vector Machines

Intelligent Transportation System (ITS) is a system that builds a safe, effective and integrated transportation environment based on advanced technologies. Road signs detection and recognition is an important part of ITS, which offer ways to collect the real time traffic data for processing at a central facility.This project is to implement a road sign recognition model based on AI and image analysis technologies, which applies a machine learning method, Support Vector Machines, to recognize road signs. We focus on recognizing seven categories of road sign shapes and five categories of speed limit signs. Two kinds of features, binary image and Zernike moments, are used for representing the data to the SVM for training and test. We compared and analyzed the performances of SVM recognition model using different features and different kernels. Moreover, the performances using different recognition models, SVM and Fuzzy ARTMAP, are observed.

Software Tools For Large Scale Interactive Hydrodynamic Modeling

Running hydrodynamic models interactively allows both visual exploration and change of model state during simulation. One of the main characteristics of an interactive model is that it should provide immediate feedback to the user, for example respond to changes in model state or view settings. For this reason, such features are usually only available for models with a relatively small number of computational cells, which are used mainly for demonstration and educational purposes. It would be useful if interactive modeling would also work for models typically used in consultancy projects involving large scale simulations. This results in a number of technical challenges related to the combination of the model itself and the visualisation tools (scalability, implementation of an appropriate API for control and access to the internal state). While model parallelisation is increasingly addressed by the environmental modeling community, little effort has been spent on developing a high-performance interactive environment. What can we learn from other high-end visualisation domains such as 3D animation, gaming, virtual globes (Autodesk 3ds Max, Second Life, Google Earth) that also focus on efficient interaction with 3D environments? In these domains high efficiency is usually achieved by the use of computer graphics algorithms such as surface simplification depending on current view, distance to objects, and efficient caching of the aggregated representation of object meshes. We investigate how these algorithms can be re-used in the context of interactive hydrodynamic modeling without significant changes to the model code and allowing model operation on both multi-core CPU personal computers and high-performance computer clusters.

Non-monotone insurance contracts and their empirical consequences

The goal of this paper is to show the possibility of a non-monotone relation between coverage ans risk which has been considered in the literature of insurance models since the work of Rothschild and Stiglitz (1976). We present an insurance model where the insured agents have heterogeneity in risk aversion and in lenience (a prevention cost parameter). Risk aversion is described by a continuous parameter which is correlated with lenience and for the sake of simplicity, we assume perfect correlation. In the case of positive correlation, the more risk averse agent has higher cosr of prevention leading to a higher demand for coverage. Equivalently, the single crossing property (SCP) is valid and iplies a positive correlation between overage and risk in equilibrium. On the other hand, if the correlation between risk aversion and lenience is negative, not only may the SCP be broken, but also the monotonocity of contracts, i.e., the prediction that high (low) risk averse types choose full (partial) insurance. In both cases riskiness is monotonic in risk aversion, but in the last case there are some coverage levels associated with two different risks (low and high), which implies that the ex-ante (with respect to the risk aversion distribution) correlation between coverage and riskiness may have every sign (even though the ex-post correlation is always positive). Moreover, using another instrument (a proxy for riskiness), we give a testable implication to desentangle single crossing ans non single croosing under an ex-post zero correlation result: the monotonicity of coverage as a function os riskiness. Since by controlling for risk aversion (no asymmetric information), coverage is monotone function of riskiness, this also fives a test for asymmetric information. Finally, we relate this theoretical results to empirical tests in the recent literature, specially the Dionne, Gouruéroux and Vanasse (2001) work. In particular, they found an empirical evidence that seems to be compatible with asymmetric information and non single crossing in our framework. More generally, we build a hidden information model showing how omitted variables (asymmetric information) can bias the sign of the correlation of equilibrium variables conditioning on all observable variables. We show that this may be the case when the omitted variables have a non-monotonic relation with the observable ones. Moreover, because this non-dimensional does not capture this deature. Hence, our main results is to point out the importance of the SPC in testing predictions of the hidden information models.

Os efeitos da concentração regional no desempenho das empresas: uma abordagem multinível

O presente trabalho tem por objetivo avaliar o impacto das concentrações regionais no desempenho organizacional das empresas brasileiras com ênfase no setor serviços. Com o intuito de atingir este objetivo realizou-se uma comparação entre o desempenho organizacional das firmas localizadas em áreas de concentração geográficas e aquelas situadas fora destas áreas. Além disso, procurou-se contrastar o efeito da concentração regional sobre o desempenho das empresas de serviços com as empresas do setor industrial. A revisão literária evidenciou a existência de vantagens para empresas concentradas regionalmente, o que levou à principal hipótese deste trabalho, de que tais vantagens ocasionariam melhor desempenho das firmas. Desta forma, buscou-se averiguar a existência de uma relação entre o desempenho organizacional e a localização geográfica das empresas de serviços regionalmente concentradas. O trabalho de identificação das concentrações regionais foi realizado adaptando-se os critérios utilizados no setor industrial para o setor serviços, a partir dos dados de número de estabelecimentos e de funcionários, obtidos através da base dados da Relação Anual de Informações Sociais (RAIS). O desempenho organizacional foi mensurado por dois indicadores: lucratividade e o crescimento de vendas. A fonte de dados de desempenho utilizada foi a base de microdados das seguintes pesquisas do Instituto Brasileiro de Geografia e Estatística (IBGE): Pesquisa Industrial Anual (PIA) e Pesquisa Anual de Serviços (PAS). A amostra utilizada incluiu 78.789 observações de prestadoras de serviços e 22.460 observações de empresas do setor industrial, entre 2001 e 2005. Os resultados foram produzidos por meio da aplicação dos modelos hierárquicos ou modelos multiníveis. Os resultados revelaram um efeito positivo sobre o crescimento das empresas situadas em áreas de concentração regional (tanto do setor serviços quanto da indústria), porém não foram encontradas evidências de maior lucratividade das mesmas. As conclusões deste trabalho contribuem para a tomada de decisão dos gestores, ao avaliar se deverão ou não situar seu empreendimento em uma área de concentração regional. Além de apresentar implicações para as políticas públicas, pois a constatação de um efeito positivo sobre o crescimento das firmas em determinadas concentrações pode direcionar políticas de incentivo, com o objetivo de estimular a formação de tais concentrações em determinadas localidades para desenvolvimento regional.

Non-monotone insurence contracts and their empirical consequences

The goal of t.his paper is to show the possibility of a non-monot.one relation between coverage and risk which has been considered in the literature of insurance models since the work of Rothschild and Stiglitz (1976). We present an insurance model where the insured agents have heterogeneity in risk aversion and in lenience (a prevention cost parameter). Risk aversion is described by a continuou.'l parameter which is correlated with lenience and, for the sake of simplicity, we assume perfect correlation. In the case of positive correlation, the more risk averse agent has higher cost of prevention leading to a higher demand for coverage. Equivalently, the single crossing property (SCP) is valid and implies a positive correlation between coverage and risk in equilibrium. On the other hand, if the correlation between risk aversion and lenience is negative, not only may the sep be broken, but also the monotonicity of contracts, i.e., the prediction that high (Iow) risk averse types choose full (partial) insurance. In both cases riskiness is monotonic in risk aversion, but in the last case t,here are some coverage leveIs associated with two different risks (low and high), which implies that the ex-ante (with respect to the risk aversion distribution) correlation bet,ween coverage and riskiness may have every sign (even though the ex-post correlation is always positive). Moreover, using another instrument (a proxy for riskiness), we give a testable implication to disentangle single crossing and non single crossing under an ex-post zero correlation result: the monotonicity of coverage as a function of riskiness. Since by controlling for risk aversion (no asymmetric informat, ion), coverage is a monotone function of riskiness, this also gives a test for asymmetric information. Finally, we relate this theoretical results to empirica! tests in the recent literature, specially the Dionne, Gouriéroux and Vanasse (2001) work. In particular, they found an empirical evidence that seems to be compatible with asymmetric information and non single crossing in our framework. More generally, we build a hidden information model showing how omitted variabIes (asymmetric information) can bias the sign of the correlation of equilibrium variabIes conditioning on ali observabIe variabIes. We show that this may be t,he case when the omitted variabIes have a non-monotonic reIation with t,he observable ones. Moreover, because this non-monotonic reIat,ion is deepIy reIated with the failure of the SCP in one-dimensional screening problems, the existing lit.erature on asymmetric information does not capture t,his feature. Hence, our main result is to point Out the importance of t,he SCP in testing predictions of the hidden information models.

Crenças normativas e comportamentais e suas influências na intenção de adoção de um sistema de informação: um estudo na área de compras da UFPA

The study aims to identify the factors that influence the behavior intention to adopt an academic Information System (SIE), in an environment of mandatory use, applied in the procurement process at the Federal University of Pará (UFPA). For this, it was used a model of innovation adoption and technology acceptance (TAM), focused in attitudes and intentions regarding the behavior intention. The research was conducted a quantitative survey, through survey in a sample of 96 administrative staff of the researched institution. For data analysis, it was used structural equation modeling (SEM), using the partial least squares method (Partial Least Square PLS-PM). As to results, the constructs attitude and subjective norms were confirmed as strong predictors of behavioral intention in a pre-adoption stage. Despite the use of SIE is required, the perceived voluntariness also predicts the behavior intention. Regarding attitude, classical variables of TAM, like as ease of use and perceived usefulness, appear as the main influence of attitude towards the system. It is hoped that the results of this study may provide subsidies for more efficient management of the process of implementing systems and information technologies, particularly in public universities

Previsão do índice bovespa por meio de redes neurais artificiais: uma análise comparada aos métodos tradicionais de séries de tempo

Forecast is the basis for making strategic, tactical and operational business decisions. In financial economics, several techniques have been used to predict the behavior of assets over the past decades.Thus, there are several methods to assist in the task of time series forecasting, however, conventional modeling techniques such as statistical models and those based on theoretical mathematical models have produced unsatisfactory predictions, increasing the number of studies in more advanced methods of prediction. Among these, the Artificial Neural Networks (ANN) are a relatively new and promising method for predicting business that shows a technique that has caused much interest in the financial environment and has been used successfully in a wide variety of financial modeling systems applications, in many cases proving its superiority over the statistical models ARIMA-GARCH. In this context, this study aimed to examine whether the ANNs are a more appropriate method for predicting the behavior of Indices in Capital Markets than the traditional methods of time series analysis. For this purpose we developed an quantitative study, from financial economic indices, and developed two models of RNA-type feedfoward supervised learning, whose structures consisted of 20 data in the input layer, 90 neurons in one hidden layer and one given as the output layer (Ibovespa). These models used backpropagation, an input activation function based on the tangent sigmoid and a linear output function. Since the aim of analyzing the adherence of the Method of Artificial Neural Networks to carry out predictions of the Ibovespa, we chose to perform this analysis by comparing results between this and Time Series Predictive Model GARCH, developing a GARCH model (1.1).Once applied both methods (ANN and GARCH) we conducted the results' analysis by comparing the results of the forecast with the historical data and by studying the forecast errors by the MSE, RMSE, MAE, Standard Deviation, the Theil's U and forecasting encompassing tests. It was found that the models developed by means of ANNs had lower MSE, RMSE and MAE than the GARCH (1,1) model and Theil U test indicated that the three models have smaller errors than those of a naïve forecast. Although the ANN based on returns have lower precision indicator values than those of ANN based on prices, the forecast encompassing test rejected the hypothesis that this model is better than that, indicating that the ANN models have a similar level of accuracy . It was concluded that for the data series studied the ANN models show a more appropriate Ibovespa forecasting than the traditional models of time series, represented by the GARCH model

Random regressions models to describe the genetic variation of milk yield over multiple parities in Buffaloes

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Random regression analyses using B-splines functions to model growth from birth to adult age in Canchim cattle

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Random regression models to estimate genetic parameters for test-day milk yield in Brazilian Murrah buffaloes

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

A framework for monitoring and runtime recovery of web service-based applications

Service provisioning is a challenging research area for the design and implementation of autonomic service-oriented software systems. It includes automated QoS management for such systems and their applications. Monitoring, Diagnosis and Repair are three key features of QoS management. This work presents a self-healing Web service-based framework that manages QoS degradation at runtime. Our approach is based on proxies. Proxies act on meta-level communications and extend the HTTP envelope of the exchanged messages with QoS-related parameter values. QoS Data are filtered over time and analysed using statistical functions and the Hidden Markov Model. Detected QoS degradations are handled with proxies. We experienced our framework using an orchestrated electronic shop application (FoodShop).

Ps-He scattering using the three-state positronium close-coupling approximation

A three-state target elastic positronium close-coupling approximation (CCA) is employed to investigate Ps-He scattering in the energy range 0-200 eV with and without electron exchange. Low-lying phase shifts below the first excitation threshold and the total integrated cross sections using both the models are reported. Estimation of integrated excitation cross sections for Ps(1s --> 2s) and Ps(1s --> 2p) using CCA are presented for the first time. The present total cross sections are in good agreement with the measured data in the incident Ps energy range 20-30 eV.