A Continuous Time Markov Chain Based Sequential Analytical Approach for Composite Power System Reliability Assessment

This paper proposes a continuous time Markov chain (CTMC) based sequential analytical approach for composite generation and transmission systems reliability assessment. The basic idea is to construct a CTMC model for the composite system. Based on this model, sequential analyses are performed. Various kinds of reliability indices can be obtained, including expectation, variance, frequency, duration and probability distribution. In order to reduce the dimension of the state space, traditional CTMC modeling approach is modified by merging all high order contingencies into a single state, which can be calculated by Monte Carlo simulation (MCS). Then a state mergence technique is developed to integrate all normal states to further reduce the dimension of the CTMC model. Moreover, a time discretization method is presented for the CTMC model calculation. Case studies are performed on the RBTS and a modified IEEE 300-bus test system. The results indicate that sequential reliability assessment can be performed by the proposed approach. Comparing with the traditional sequential Monte Carlo simulation method, the proposed method is more efficient, especially in small scale or very reliable power systems.

Contamination of Donana food-chains after the Aznalcollar mine disaster

On 25 April 1998 part of the tailings pond dike of the Aznalcollar Zn mine north of the Guadalquivir marshes (Donana) in southern Spain collapsed releasing an estimated 5 million m³ of acidic metal-rich waste. This event contaminated farmland and wetland up to >40 km downstream, including the 900-ha 'Entremuros', an important area for birds within the Donana world heritage site. In spite of the contamination, birds continued to feed in this area. Samples of two abundant macrophytes (Typha dominguensis and Scirpus maritimus) were taken from the Entremuros and nearby uncontaminated areas; these plants are important food items for several bird species. Analyses showed that in the Entremuros mean plant tissue concentrations of Cd were 3-40-fold (0.8-7.4 ppm) and Zn 20-100-fold (20-3384 ppm) greater than those from control areas. Comparable dietary concentrations of Zn have been reported to cause severe physiological damage to aquatic birds under experimental conditions. Elevated Cd concentrations are of concern as Cd bioconcentrates and is a cumulative poison. Metals released in this accident are moving into this food-chain and present a considerable risk to species feeding on Typha sp. and Scirpus sp. Many other food-webs exist in this area and require detailed examination to identify the species at risk, and to facilitate the management of these risks to minimise future impacts to the wildlife of Donana. Copyright (C) 1999 Elsevier Science Ltd.

Using the hidden Markov model to capture quality of care in Lombardy geriatric wards

Quality of care is an important aspect of healthcare monitoring, which is used to ensure that the healthcare system is delivering care of the highest standard. With populations growing older there is an increased urgency in making sure that the healthcare delivered is of the highest standard. Healthcare providers are under increased pressure to ensure that this is the case with public and government demand expecting a healthcare system of the highest quality. Modelling quality of care is difficult to measure due to the many ways of defining it. This paper introduces a potential model which could be used to take quality of care into account when modelling length of stay. The Coxian phase-type distribution is used to model length of stay and the associated quality of care incorporated into the Coxian using a Hidden Markov model. Covariates are also introduced to determine their impact on the hidden level to find out what potentially can affect quality of care. This model is applied to geriatic patient data from the Lombardy region of Italy. The results obtained highlighted that bed numbers and the type of hospital (public or private) can have an effect on the quality of care delivered.

Application of disturbance management principles in fresh food supply chains

The desire for more robust supply chains has led to a growth in theoretical and practical interest in the application of both preventive and impact reductive disturbance management principles. This application should ultimately lead to less vulnerable and more competitive supply chains. Based on the extant literature we identify fresh food supply chain's contextual factors (products, processes, supply chain networks and supply chain business environment) and their corresponding characteristics, as well as the main disturbance management principles used. To analyse their influence on the selection and application of disturbance management principles in fresh food supply chains we conducted three case studies. In each case we collected data on the relevant contextual factors, disturbance management principles applied and company background. As an underlying methodology, we first conduct within-case analysis and then expand the analyses to a cross-case context. Based on the findings from these case studies, propositions are built concerning the nature of contextual factors and their characteristics, and their influence on the selection and application of disturbance management principles in fresh food supply chains. Our main findings are related to the identification of contextual characteristics of fresh food supply chains that are either critical vulnerability sources, critical enablers or conditionals and as such require, facilitate or condition selection and application of disturbance management principles.

Combining Bio- and Chemo-Catalysis for the Conversion of Bio-Renewable Alcohols: Homogeneous Iridium Catalysed Hydrogen Transfer Initiated Dehydration of 1,3-Propanediol to Aldehydes

Combining whole cell biocatalysis and chemocatalysis in a single reaction sequence avoids unnecessary separations, and the associated waste and energy consumption. Bacterial fermentation has been employed to convert waste glycerol from biodiesel production into 1,3-propanediol. This 1,3-propanediol can be extracted selectively from the aqueous fermentation broth using ionic liquids. 1,3-propanediol in ionic liquid solution was converted to propanal by hydrogen transfer initiated dehydration (HTID) catalysed by a Cp*IrCl2(NHC) (Cp* = pentamethylcyclopentadienyl; NHC = carbene ligand) complex. The use of an ionic liquid solvent enabled the reaction to be performed under reduced pressure, facilitating the isolation of the product, and improving the reaction selectivity. The Ir(III) catalyst in ionic liquid was found to be highly recyclable.

Detecting localized homogeneous anomalies over spatio-temporal data

The last decade has witnessed an unprecedented growth in availability of data having spatio-temporal characteristics. Given the scale and richness of such data, finding spatio-temporal patterns that demonstrate significantly different behavior from their neighbors could be of interest for various application scenarios such as – weather modeling, analyzing spread of disease outbreaks, monitoring traffic congestions, and so on. In this paper, we propose an automated approach of exploring and discovering such anomalous patterns irrespective of the underlying domain from which the data is recovered. Our approach differs significantly from traditional methods of spatial outlier detection, and employs two phases – i) discovering homogeneous regions, and ii) evaluating these regions as anomalies based on their statistical difference from a generalized neighborhood. We evaluate the quality of our approach and distinguish it from existing techniques via an extensive experimental evaluation.

Experimental Saltwater Intrusion in Coastal aquifers Using Automated Image Analysis: Applications to Homogeneous Aquifers.

This paper presents the applications of a novel methodology to quantify saltwater intrusion parameters in laboratory-scale experiments. The methodology uses an automated image analysis procedure, minimizing manual inputs and the subsequent systematic errors that can be introduced. This allowed the quantification of the width of the mixing zone which is difficult to measure in experimental methods that are based on visual observations. Glass beads of different grain sizes were tested for both steady-state and transient conditions. The transient results showed good correlation between experimental and numerical intrusion rates. The experimental intrusion rates revealed that the saltwater wedge reached a steady state condition sooner while receding than advancing. The hydrodynamics of the experimental mixing zone exhibited similar
traits; a greater increase in the width of the mixing zone was observed in the receding saltwater wedge, which indicates faster fluid velocities and higher dispersion. The angle of intrusion analysis revealed the formation of a volume of diluted saltwater at the toe position when the saltwater wedge is prompted to recede. In addition, results of different physical repeats of the experiment produced an average coefficient of variation less than 0.18 of the measured toe length and width of the mixing zone.

Hidden Markov Models With Set-Valued Parameters

Hidden Markov models (HMMs) are widely used probabilistic models of sequential data. As with other probabilistic models, they require the specification of local conditional probability distributions, whose assessment can be too difficult and error-prone, especially when data are scarce or costly to acquire. The imprecise HMM (iHMM) generalizes HMMs by allowing the quantification to be done by sets of, instead of single, probability distributions. iHMMs have the ability to suspend judgment when there is not enough statistical evidence, and can serve as a sensitivity analysis tool for standard non-stationary HMMs. In this paper, we consider iHMMs under the strong independence interpretation, for which we develop efficient inference algorithms to address standard HMM usage such as the computation of likelihoods and most probable explanations, as well as performing filtering and predictive inference. Experiments with real data show that iHMMs produce more reliable inferences without compromising the computational efficiency.

Using a Discrete Hidden Markov Model Kernel for lip-based biometric identification

In this paper, a novel and effective lip-based biometric identification approach with the Discrete Hidden Markov Model Kernel (DHMMK) is developed. Lips are described by shape features (both geometrical and sequential) on two different grid layouts: rectangular and polar. These features are then specifically modeled by a DHMMK, and learnt by a support vector machine classifier. Our experiments are carried out in a ten-fold cross validation fashion on three different datasets, GPDS-ULPGC Face Dataset, PIE Face Dataset and RaFD Face Dataset. Results show that our approach has achieved an average classification accuracy of 99.8%, 97.13%, and 98.10%, using only two training images per class, on these three datasets, respectively. Our comparative studies further show that the DHMMK achieved a 53% improvement against the baseline HMM approach. The comparative ROC curves also confirm the efficacy of the proposed lip contour based biometrics learned by DHMMK. We also show that the performance of linear and RBF SVM is comparable under the frame work of DHMMK.

Multilinear and Integer Programming for Markov Decision Processes with Imprecise Probabilities

Markov Decision Processes (MDPs) are extensively used to encode sequences of decisions with probabilistic effects. Markov Decision Processes with Imprecise Probabilities (MDPIPs) encode sequences of decisions whose effects are modeled using sets of probability distributions. In this paper we examine the computation of Γ-maximin policies for MDPIPs using multilinear and integer programming. We discuss the application of our algorithms to “factored” models and to a recent proposal, Markov Decision Processes with Set-valued Transitions (MDPSTs), that unifies the fields of probabilistic and “nondeterministic” planning in artificial intelligence research. 

Moralising Gold Supply Chains

During the late twentieth century the supply chains for gold were considered by the majority of consumers (when they were considered at all) to be driven by simple commercial imperatives. That notion was shattered during the first decade of the twenty-first century by the appearance of ethical campaigns, led by advocates determined to present major players in the gold industry as morally reprehensible. The ‘No Dirty Gold’ campaign sought to shift the purchasing of gold onto a moral register, in order to challenge the activities of large mining corporations. It was followed by the Fairtrade Foundation’s ‘Fairtrade Gold’ initiative, which had aspirations to support subsistence mining communities at the expense of big business. By directly targeting a luxury material and playing on its inherent social ambiguities, campaigners hoped to thoroughly moralise the purchasing of gold objects. Dr Oakley’s presentation will examine the forces behind this developing social phenomenon, describe the trajectories of a selection of major campaigns, and consider the extent to which these have impacted on public attitudes, gold miners and the actions of consumers, producers and retailers of luxury goods.

A desagregação espacial de dados agrícolas: aplicação de uma abordagem baseada na máxima entropia ao concelho de Castelo de Vide

Dissertação mest., Gestão Sustentável de Espaços Rurais, Universidade do Algarve, 2009