5 research outputs found

    Evaluation of supply chain risk management for material procurement in Libyan oil industry

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    The oil industry is considered to be a major industry as it provides energy to all other industries. This industry is exposed to various risks due to extreme circumstances, such as remote area locations, harsh environment, equipment, and functional engineering materials that are exclusively manufactured for this industry. All of these circumstances can disrupt and threaten the existences of the industry. This is where risk management and supply chain risk management is vitally needed by researchers and practitioners. Therefore, the assessment and prediction of the impact of risks on the procurement operation performance of projects is a very challenging task. As a result of this, many projects in the Libyan oil industry and worldwide are still suffering from the impact of these risks. The aspect of risk in supply chain management is underdeveloped on the body of literature, and very few studies have addressed this issue due to its confidentiality. The purpose of this research is to investigate the role of supply-chain risk management in the Libyan oil industry and to understand how procurement practitioners assess supply chain risk management to achieve smooth procurement operations. The author derives a set of propositions and preliminary results which contribute to developing strategies to identify and mitigate those risks. Hence, the contribution to knowledge is to investigate these issues within the Libyan oil industry and also to develop a framework that can be used as a risk management supporting tool. Qualitative and quantitative (triangulation) were adopted for this research. This comprised of the investigation of 65 out of 93 Libyan procurement practitioners, as well as interviews of which 9 Libyan procurement practitioners participated. This research finds that service providers and contractor companies are the highest percentage within stakeholders, who are practising supply risk management techniques. However, this percentage is still low within its group. This research also identifies types of risks that majorly affect the performance of procurement operations, such as purchasing clone parts. Thus, providing valuable information for particular stages of response planning. It also explores how the consideration of risk management can reshape supply chain management. Moreover, a Procurement Risk Management Framework (PRMF) that has been developed and empirically validated

    Managing complex systems: an interdisciplinary approach to modelling the effect of social and ecological interactions on carbon storage in blanket peatlands.

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    Peatlands are globally important for carbon storage, water quality and biodiversity. However, many have been degraded by land use, and efforts to conserve or restore them are often contested by stakeholders with different objectives. Peat accumulates as a result of a complex network of interactions, which makes it challenging to predict the impact of climate and land use. Stakeholders’ knowledge may help to provide insights into these interactions and into the issues that underpin conflict. To investigate the impact of social and ecological factors on blanket peatland carbon storage, an interdisciplinary approach was used to couple cognitive and peatland development models. Blanket peatland stakeholders developed fuzzy cognitive maps based on their perceptions of peatland interactions, which they validated to agree on the structure of an aggregate network. To explore the impact of land–use objectives on carbon stocks, stakeholders proposed changes to a set of factors that controlled the state of the network. The changes identified to improve carbon storage and water quality had a positive effect on carbon stored, but those that were proposed to support local livelihoods had no effect on carbon. This was partly because some stakeholders perceived that supporting livelihoods was incompatible with measures that were likely to result in shallower water tables. However, further discussions between stakeholders suggested that land–use objectives could complement each other. To enrich the results of the network model, the DigiBog peatland model was modified to simulate blanket peat accumulation. Using two factors from the cognitive model, climate change and gully blocking, two novel modelling studies were produced. The first showed that existing peatland development models may overestimate peat accumulation because they aggregate climate variables into annual rather than weekly inputs; the second, that gully blocking is needed to arrest peat losses from oxidation in gullied systems, but that these losses would not be recovered 200 years after gully blocking. The combination of both cognitive and process–based modelling provides an example of how stakeholder knowledge can be incorporated into simulations of complex ecosystems which is likely to be applicable to other social–ecological systems where land use is contested. In this case, doing so provided holistic insights into how stakeholders’ perceptions, and the impacts of climatic forcing and restoration, affect carbon storage in blanket peatlands

    Hierarchical categorisation of tags for delicious

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    In the scenario of social bookmarking, a user browsing the Web bookmarks web pages and assigns free-text labels (i.e., tags) to them according to their personal preferences. In this technical report, we approach one of the practical aspects when it comes to represent users' interests from their tagging activity, namely the categorization of tags into high-level categories of interest. The reason is that the representation of user profiles on the basis of the myriad of tags available on the Web is certainly unfeasible from various practical perspectives; mainly concerning the unavailability of data to reliably, accurately measure interests across such fine-grained categorisation, and, should the data be available, its overwhelming computational intractability. Motivated by this, our study presents the results of a categorization process whereby a collection of tags posted at Delicious #http://delicious.com# are classified into 200 subcategories of interest.Preprin

    Hierarchical categorisation of web tags for Delicious

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    In the scenario of social bookmarking, a user browsing the Web bookmarks web pages and assigns free-text labels (i.e., tags) to them according to their personal preferences. The benefits of social tagging are clear – tags enhance Web content browsing and search. However, since these tags may be publicly available to any Internet user, a privacy attacker may collect this information and extract an accurate snapshot of users’ interests or user profiles, containing sensitive information, such as health-related information, political preferences, salary or religion. In order to hinder attackers in their efforts to profile users, this report focuses on the practical aspects of capturing user interests from their tagging activity. More accurately, we study how to categorise a collection of tags posted by users in one of the most popular bookmarking services, Delicious (http://delicious.com).Preprin

    Multilevel Power Estimation Of VLSI Circuits Using Efficient Algorithms

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    New and complex systems are being implemented using highly advanced Electronic Design Automation (EDA) tools. As the complexity increases day by day, the dissipation of power has emerged as one of the very important design constraints. Now low power designs are not only used in small size applications like cell phones and handheld devices but also in high-performance computing applications. Embedded memories have been used extensively in modern SOC designs. In order to estimate the power consumption of the entire design correctly, an accurate memory power model is needed. However, the memory power model commonly used in commercial EDA tools is too simple to estimate the power consumption accurately. For complex digital circuits, building their power models is a popular approach to estimate their power consumption without detailed circuit information. In the literature, most of power models are built with lookup tables. However, building the power models with lookup tables may become infeasible for large circuits because the table size would increase exponentially to meet the accuracy requirement. This thesis involves two parts. In first part it uses the Synopsys power measurement tools together with the use of synthesis and extraction tools to determine power consumed by various macros at different levels of abstraction including the Register Transfer Level (RTL), the gate and the transistor level. In general, it can be concluded that as the level of abstraction goes down the accuracy of power measurement increases depending on the tool used. In second part a novel power modeling approach for complex circuits by using neural networks to learn the relationship between power dissipation and input/output characteristic vector during simulation has been developed. Our neural power model has very low complexity such that this power model can be used for complex circuits. Using such a simple structure, the neural power models can still have high accuracy because they can automatically consider the non-linear power distributions. Unlike the power characterization process in traditional approaches, our characterization process is very simple and straightforward. More importantly, using the neural power model for power estimation does not require any transistor-level or gate-level description of the circuits. The experimental results have shown that the estimations are accurate and efficient for different test sequences with wide range of input distributions
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