Risk Management in the Arctic Offshore: Wicked Problems Require New Paradigms

Recent project-management literature and high-profile disasters—the financial crisis, the BP Deepwater Horizon oil spill, and the Fukushima nuclear accident—illustrate the flaws of traditional risk models for complex projects. This research examines how various groups with interests in the Arctic offshore define risks. The findings link the wicked problem framework and the emerging paradigm of Project Management of the Second Order (PM-2). Wicked problems are problems that are unstructured, complex, irregular, interactive, adaptive, and novel. The authors synthesize literature on the topic to offer strategies for navigating wicked problems, provide new variables to deconstruct traditional risk models, and integrate objective and subjective schools of risk analysis

Performance Measures Using Electronic Health Records: Five Case Studies

Presents the experiences of five provider organizations in developing, testing, and implementing four types of electronic quality-of-care indicators based on EHR data. Discusses challenges, and compares results with those from traditional indicators

Application of Machine Learning Methods for Asset Management on Power Distribution Networks

This study aims to study the different kinds of Machine Learning (ML) models and their working principles for asset management in power networks. Also, it investigates the challenges behind asset management and its maintenance activities. In this review article, Machine Learning (ML) models are analyzed to improve the lifespan of the electrical components based on the maintenance management and assessment planning policies. The articles are categorized according to their purpose: 1) classification, 2) machine learning, and 3) artificial intelligence mechanisms. Moreover, the importance of using ML models for proper decision making based on the asset management plan is illustrated in a detailed manner. In addition to this, a comparative analysis between the ML models is performed, identifying the advantages and disadvantages of these techniques. Then, the challenges and managing operations of the asset management strategies are discussed based on the technical and economic factors. The proper functioning, maintenance and controlling operations of the electric components are key challenging and demanding tasks in the power distribution systems. Typically, asset management plays an essential role in determining the quality and profitability of the elements in the power network. Based on this investigation, the most suitable and optimal machine learning technique can be identified and used for future work. Doi: 10.28991/ESJ-2022-06-04-017 Full Text: PD

Systematic identification of functional plant modules through the integration of complementary data sources

A major challenge is to unravel how genes interact and are regulated to exert specific biological functions. The integration of genome-wide functional genomics data, followed by the construction of gene networks, provides a powerful approach to identify functional gene modules. Large-scale expression data, functional gene annotations, experimental protein-protein interactions, and transcription factor-target interactions were integrated to delineate modules in Arabidopsis (Arabidopsis thaliana). The different experimental input data sets showed little overlap, demonstrating the advantage of combining multiple data types to study gene function and regulation. In the set of 1,563 modules covering 13,142 genes, most modules displayed strong coexpression, but functional and cis-regulatory coherence was less prevalent. Highly connected hub genes showed a significant enrichment toward embryo lethality and evidence for cross talk between different biological processes. Comparative analysis revealed that 58% of the modules showed conserved coexpression across multiple plants. Using module-based functional predictions, 5,562 genes were annotated, and an evaluation experiment disclosed that, based on 197 recently experimentally characterized genes, 38.1% of these functions could be inferred through the module context. Examples of confirmed genes of unknown function related to cell wall biogenesis, xylem and phloem pattern formation, cell cycle, hormone stimulus, and circadian rhythm highlight the potential to identify new gene functions. The module-based predictions offer new biological hypotheses for functionally unknown genes in Arabidopsis (1,701 genes) and six other plant species (43,621 genes). Furthermore, the inferred modules provide new insights into the conservation of coexpression and coregulation as well as a starting point for comparative functional annotation

Multiple goals and time constraints : perceived impact on physicians' performance of evidence-based behaviours

Peer reviewedPublisher PD

Embedding knowledge and value of a brand into sustainability for differentiation

This is the post-print version of the final paper published in the Journal of World Business (under the provisional title "Embedding sustainability into brand knowledge and brand value for brand differentiation"). The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2012 Elsevier B.V.Organisations offer products to consumers, buyers often question if the product or its production process are linked to the environmental, social or economic challenges being faced by mankind. Inquisitiveness of customers in this direction points towards an opportunity for marketers to create differentiation based on the concerns of brand towards overall issue of sustainability. The authors have synthesized knowledge from various domains with a positivistic approach to understand sustainability from the perspective of branding. Using empirical knowledge this study recommends embedding sustainability into brand knowledge and brand value for creating a differentiation for the brand in a competitive market

System design and maintenance modelling for safety in extended life operation

It is frequently the most cost effective option to operate systems and infrastructure over an extended life period rather than enter a new build programme. The condition and performance of existing systems operated beyond their originally intended design life are controlled through maintenance. For new systems there is the option to simultaneously develop the design and the maintenance processes for best effect when a longer life expectancy is planned. This paper reports a combined Petri net and Bayesian network approach to investigate the effects of design and maintenance features on the system performance. The method has a number of features which overcome limitations in traditionally used system performance modelling techniques, such as fault tree analysis, and also enhances the modelling capabilities. Significantly, for the assessment of aging systems, the new method avoids the need to assume a constant failure rate over the lifetime duration. In addition the assumption of independence between component failures events is no longer required. In comparison with the commonly applied system modelling techniques, this new methodology also has the capability to represent the maintenance process in far greater detail and as such options for: inspection and testing, servicing, reactive repair and component replacement based on condition, age or use can all be included. In considering system design options, levels of redundancy and diversity along with the component types selected can be investigated. All of the options for the design and maintenance can be incorporated into a single integrated Petri net and Bayesian network model and turned on and off as required to predict the effects of any combination of options selected. In addition this model has the ability to evaluate different system failure modes. The integrated Petri-net and Bayesian network approach is demonstrated through application to a remote un-manned wellhead platform from the oil and gas industry

Operations Management

Global competition has caused fundamental changes in the competitive environment of the manufacturing and service industries. Firms should develop strategic objectives that, upon achievement, result in a competitive advantage in the market place. The forces of globalization on one hand and rapidly growing marketing opportunities overseas, especially in emerging economies on the other, have led to the expansion of operations on a global scale. The book aims to cover the main topics characterizing operations management including both strategic issues and practical applications. A global environmental business including both manufacturing and services is analyzed. The book contains original research and application chapters from different perspectives. It is enriched through the analyses of case studies