Reconfiguration of distribution network into islanded microgrids considering development of distributed energy resources

This paper discusses the potentiality of reconfiguring distribution networks into islanded Microgrids to reduce the network infrastructure reinforcement requirement and incorporate various dispersed energy resources. The major challenge would be properly breaking down the network and its resultant protection and automation system changes. A reconfiguration method is proposed based on allocation of distributed generation resources to fulfil this purpose, with a heuristic algorithm. Cost/reliability data is required for the next stage tasks to realise a case study of a particular network.

Dynamic Bayesian network for semantic place classification in mobile robotics

In this paper, the problem of semantic place categorization in mobile robotics is addressed by considering a time-based probabilistic approach called dynamic Bayesian mixture model (DBMM), which is an improved variation of the dynamic Bayesian network. More specifically, multi-class semantic classification is performed by a DBMM composed of a mixture of heterogeneous base classifiers, using geometrical features computed from 2D laserscanner data, where the sensor is mounted on-board a moving robot operating indoors. Besides its capability to combine different probabilistic classifiers, the DBMM approach also incorporates time-based (dynamic) inferences in the form of previous class-conditional probabilities and priors. Extensive experiments were carried out on publicly available benchmark datasets, highlighting the influence of the number of time-slices and the effect of additive smoothing on the classification performance of the proposed approach. Reported results, under different scenarios and conditions, show the effectiveness and competitive performance of the DBMM.

Bayesian neural network approach for determining the risk of re-intervention after endovascular aortic aneurysm repair

This article proposes a Bayesian neural network approach to determine the risk of re-intervention after endovascular aortic aneurysm repair surgery. The target of proposed technique is to determine which patients have high chance to re-intervention (high-risk patients) and which are not (low-risk patients) after 5 years of the surgery. Two censored datasets relating to the clinical conditions of aortic aneurysms have been collected from two different vascular centers in the United Kingdom. A Bayesian network was first employed to solve the censoring issue in the datasets. Then, a back propagation neural network model was built using the uncensored data of the first center to predict re-intervention on the second center and classify the patients into high-risk and low-risk groups. Kaplan-Meier curves were plotted for each group of patients separately to show whether there is a significant difference between the two risk groups. Finally, the logrank test was applied to determine whether the neural network model was capable of predicting and distinguishing between the two risk groups. The results show that the Bayesian network used for uncensoring the data has improved the performance of the neural networks that were built for the two centers separately. More importantly, the neural network that was trained with uncensored data of the first center was able to predict and discriminate between groups of low risk and high risk of re-intervention after 5 years of endovascular aortic aneurysm surgery at center 2 (p = 0.0037 in the logrank test).

Searching for resilience in sustainable development:learning journeys in conservation

Resilience is a term that is gaining currency in conservation and sustainable development, though its meaning and value in this context is yet to be defined. Searching for Resilience in Sustainable Development examines ways in which resilience may be created within the web of ecological, socio-economic and cultural systems that make up the world in. The authors embark upon a learning journey exploring both robust and fragile systems and asking questions of groups and individuals actively involved in building or maintaining resilience.

Managing risk in software development projects:a case study

Purpose - The main objective of the paper is to develop a risk management framework for software development projects from developers' perspective. Design/methodology/approach - This study uses a combined qualitative and quantitative technique with the active involvement of stakeholders in order to identify, analyze and respond to risks. The entire methodology has been explained using a case study on software development project in a public sector organization in Barbados. Findings - Analytical approach to managing risk in software development ensures effective delivery of projects to clients. Research limitations/implications - The proposed risk management framework has been applied to a single case. Practical implications - Software development projects are characterized by technical complexity, market and financial uncertainties and competent manpower availability. Therefore, successful project accomplishment depends on addressing those issues throughout the project phases. Effective risk management ensures the success of projects. Originality/value - There are several studies on managing risks in software development and information technology (IT) projects. Most of the studies identify and prioritize risks through empirical research in order to suggest mitigating measures. Although they are important to clients for future projects, these studies fail to provide any framework for risk management from software developers' perspective. Although a few studies introduced framework of risk management in software development, most of them are presented from clients' perspectives and very little effort has been made to integrate this with the software development cycle. As software developers absorb considerable amount of risks, an integrated framework for managing risks in software development from developers' perspective is needed. © Emerald Group Publishing Limited.

TATPred:a Bayesian method for the identification of twin arginine translocation pathway signal sequences

The twin arginine translocation (TAT) system ferries folded proteins across the bacterial membrane. Proteins are directed into this system by the TAT signal peptide present at the amino terminus of the precursor protein, which contains the twin arginine residues that give the system its name. There are currently only two computational methods for the prediction of TAT translocated proteins from sequence. Both methods have limitations that make the creation of a new algorithm for TAT-translocated protein prediction desirable. We have developed TATPred, a new sequence-model method, based on a Nave-Bayesian network, for the prediction of TAT signal peptides. In this approach, a comprehensive range of models was tested to identify the most reliable and robust predictor. The best model comprised 12 residues: three residues prior to the twin arginines and the seven residues that follow them. We found a prediction sensitivity of 0.979 and a specificity of 0.942.

A generative probabilistic oriented wavelet model for texture segmentation

This Letter addresses image segmentation via a generative model approach. A Bayesian network (BNT) in the space of dyadic wavelet transform coefficients is introduced to model texture images. The model is similar to a Hidden Markov model (HMM), but with non-stationary transitive conditional probability distributions. It is composed of discrete hidden variables and observable Gaussian outputs for wavelet coefficients. In particular, the Gabor wavelet transform is considered. The introduced model is compared with the simplest joint Gaussian probabilistic model for Gabor wavelet coefficients for several textures from the Brodatz album [1]. The comparison is based on cross-validation and includes probabilistic model ensembles instead of single models. In addition, the robustness of the models to cope with additive Gaussian noise is investigated. We further study the feasibility of the introduced generative model for image segmentation in the novelty detection framework [2]. Two examples are considered: (i) sea surface pollution detection from intensity images and (ii) image segmentation of the still images with varying illumination across the scene.

Process integration and improvement using systemic diagrams and a human-centred approach

If product cycle time reduction is the mission, and the multifunctional team is the means of achieving the mission, what then is the modus operandi by which this means is to accomplish its mission? This paper asserts that a preferred modus operandi for the multifunctional team is to adopt a process-oriented view of the manufacturing enterprise, and for this it needs the medium of a process map [16] The substance of this paper is a methodology which enables the creation of such maps Specific examples of process models drawn from the product develop ment life cycle are presented and described in order to support the methodology's integrity and value The specific deliverables we have so far obtained are a methodology for process capture and analysis, a collection of process models spanning the product development cycle, and, an engineering handbook which hosts these models and presents a computer-based means of navigating through these processes in order to allow users a better understanding of the nature of the business, their role in it, and why the job that they do benefits the work of the company We assert that this kind of thinking is the essence of concurrent engineering implementation, and further that the systemigram process models uniquely stim ulate and organise such thinking.

A methodology for implementing a decision support system:a small company case study

The importance of non-technical factors in the design and implementation of information systems has been increasingly recognised by both researchers and practitioners, and recent literature highlights the need for new tools and techniques with an organisational, rather than technical, focus. The gap between what is technically possible and what is generally practised, is particularly wide in the sales and marketing field. This research describes the design and implementation of a decision support system (DSS) for marketing planning and control in a small, but complex company and examines the nature of the difficulties encountered. An intermediary with functional, rather than technical, expertise is used as a strategy for overcoming these by taking control of the whole of the systems design and implementation cycle. Given the practical nature of the research, an action research approach is adopted with the researcher undertaking this role. This approach provides a detailed case study of what actually happens during the DSS development cycle, allowing the influence of organisational factors to be captured. The findings of the research show how the main focus of the intermediary's role needs to be adapted over the systems development cycle; from coordination and liaison in the pre-design and design stages, to systems champion during the first part of the implementation stage, and finally to catalyst to ensure that the DSS is integrated into the decision-making process. Two practical marketing exercises are undertaken which illustrate the nature of the gap between the provision of information and its use. The lack of a formal approach to planning and control is shown to have a significant effect on the way the DSS is used and the role of the intermediary is extended successfully to accommodate this factor. This leads to the conclusion that for the DSS to play a fully effective role, small firms may need to introduce more structure into their marketing planning, and that the role of the intermediary, or Information Coordinator, should include the responsibility for introducing new techniques and ideas to aid with this.

A novel concentration dependent amino acid ion pair strategy to mediate drug permeation using indomethacin as a model insoluble drug

Assessment of oral drug bioavailability is an important parameter for new chemical entities (NCEs) in drug development cycle. After evaluating the pharmacological response of these new molecules, the following critical stage is to investigate their in vitro permeability. Despite the great success achieved by prodrugs, covalent linking the drug molecule with a hydrophobic moiety might result in a new entity that might be toxic or ineffective. Therefore, an alternative that would improve the drug uptake without affecting the efficacy of the drug molecule would be advantageous. The aim of the current study is to investigate the effect of ion-pairing on the permeability profile of a model drug: indomethacin (IND) to understand the mechanism behind the permeability improvement across Caco-2 monolayers. Arginine and lysine formed ion-pairs with IND at various molar ratios 1:1, 1:2, 1:4 and 1:8 as reflected by the double reciprocal graphs. The partitioning capacities of the IND were evaluated using octanol/water partitioning studies and the apparent permeabilities (P app) were measured across Caco-2 monolayers for the different formulations. Partitioning studies reflected the high hydrophobicity of IND (Log P = 3) which dropped upon increasing the concentrations of arginine/lysine in the ion pairs. Nevertheless, the prepared ion pairs improved IND permeability especially after 60 min of the start of the experiment. Coupling partitioning and permeability results suggest a decrease in the passive transcellular uptake due to the drop in IND portioning capacities and a possible involvement of active carriers. Future work will investigate which transport gene might be involved in the absorption of the ion paired formulations using molecular biology technologies. © 2014 Elsevier B.V. All rights reserved.

Negotiating regional futures:the successes and failures of the West Midlands Regional Development Agency Network

The introduction of Regional Development Agencies (RDAs) in the English regions in 1999 presented a new set of collaborative challenges to existing local institutions. The key objectives of the new policy impetus emphasise increased joined-up thinking and holistic regional governance. Partners were enjoined to promote cross-sector collaboration and present a coherent regional voice. This study aims to evaluate the impact of an RDA on the partnership infrastructure of the West Midlands. The RDA network incorporates a wide spectrum of interest and organisations with diverse collaborative histories, competencies and capacities. The study has followed partners through the process over an eighteen-month period and has sought to explore the complexities and tensions of partnership working 'on the ground'. A strong qualitative methodology has been employed in generating 'thick descriptions' of the policy domain. The research has probed beyond the 'rhetoric' of partnerships and explores the sensitivities of the collaboration process. A number of theoretical frameworks have been employed, including policy network theory; partnership and collaboration theory; organisational learning; and trust and social capital. The structural components of the West Midlands RDA network are explored, including the structural configuration of the network and stocks of human and social capital assets. These combine to form the asset base of the network. Three sets of network behaviours are then explored, namely, strategy, the management of perceptions, and learning. The thesis explores how the combination of assets and behaviours affect, and in turn are affected by, each other. The findings contribute to the growing body of knowledge and understanding surrounding policy networks and collaborative governance.

Development of a conceptual model of the soil-moisture-plant sub-system of the hydrological cycle

The soil-plant-moisture subsystem is an important component of the hydrological cycle. Over the last 20 or so years a number of computer models of varying complexity have represented this subsystem with differing degrees of success. The aim of this present work has been to improve and extend an existing model. The new model is less site specific thus allowing for the simulation of a wide range of soil types and profiles. Several processes, not included in the original model, are simulated by the inclusion of new algorithms, including: macropore flow; hysteresis and plant growth. Changes have also been made to the infiltration, water uptake and water flow algorithms. Using field data from various sources, regression equations have been derived which relate parameters in the suction-conductivity-moisture content relationships to easily measured soil properties such as particle-size distribution data. Independent tests have been performed on laboratory data produced by Hedges (1989). The parameters found by regression for the suction relationships were then used in equations describing the infiltration and macropore processes. An extensive literature review produced a new model for calculating plant growth from actual transpiration, which was itself partly determined by the root densities and leaf area indices derived by the plant growth model. The new infiltration model uses intensity/duration curves to disaggregate daily rainfall inputs into hourly amounts. The final model has been calibrated and tested against field data, and its performance compared to that of the original model. Simulations have also been carried out to investigate the effects of various parameters on infiltration, macropore flow, actual transpiration and plant growth. Qualitatively comparisons have been made between these results and data given in the literature.

Protection issue discussion of DC network development:circuit breaker or fault-tolerant converter

This paper provides a discussion on future direct current (DC) network development in terms of system protection under DC-side fault scenarios. The argument between appropriate DC circuit breaker and new DC fault-tolerant converters is discussed after a review on DC technology development and bottleneck issues that require proper solutions. The overcurrent/cost curve of power-electronic DC circuit breakers (CB) superimposed to voltage-source converter (VSC) systems is derived and compared with other possible fault-tolerant power conversion options. This in-advance planning of protection capability is essential for the future development of DC networks.

Managing supply chain knowledge in the new product development process:a social network analysis approach

The aim of this paper is to propose a conceptual framework for studying the knowledge transfer problem within the supply chain. The social network analysis (SNA) is presented as a useful tool to study knowledge networks within supply chain, to visualize knowledge flows and to identify the accumulating knowledge nodes of the networks. © 2011 IEEE.

A Bayesian neural network algorithm identifies patients in whom post-EVAR surveillance may be unnecessary

Lifelong surveillance is not cost-effective after endovascular aneurysm repair (EVAR), but is required to detect aortic complications which are fatal if untreated (type 1/3 endoleak, sac expansion, device migration). Aneurysm morphology determines the probability of aortic complications and therefore the need for surveillance, but existing analyses have proven incapable of identifying patients at sufficiently low risk to justify abandoning surveillance. This study aimed to improve the prediction of aortic complications, through the application of machine-learning techniques. Patients undergoing EVAR at 2 centres were studied from 2004–2010. Aneurysm morphology had previously been studied to derive the SGVI Score for predicting aortic complications. Bayesian Neural Networks were designed using the same data, to dichotomise patients into groups at low- or high-risk of aortic complications. Network training was performed only on patients treated at centre 1. External validation was performed by assessing network performance independently of network training, on patients treated at centre 2. Discrimination was assessed by Kaplan-Meier analysis to compare aortic complications in predicted low-risk versus predicted high-risk patients. 761 patients aged 75 +/− 7 years underwent EVAR in 2 centres. Mean follow-up was 36+/− 20 months. Neural networks were created incorporating neck angu- lation/length/diameter/volume; AAA diameter/area/volume/length/tortuosity; and common iliac tortuosity/diameter. A 19-feature network predicted aor- tic complications with excellent discrimination and external validation (5-year freedom from aortic complications in predicted low-risk vs predicted high-risk patients: 97.9% vs. 63%; p < 0.0001). A Bayesian Neural-Network algorithm can identify patients in whom it may be safe to abandon surveillance after EVAR. This proposal requires prospective study.