870 resultados para WEC deployment


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Collaborative mining of distributed data streams in a mobile computing environment is referred to as Pocket Data Mining PDM. Hoeffding trees techniques have been experimentally and analytically validated for data stream classification. In this paper, we have proposed, developed and evaluated the adoption of distributed Hoeffding trees for classifying streaming data in PDM applications. We have identified a realistic scenario in which different users equipped with smart mobile devices run a local Hoeffding tree classifier on a subset of the attributes. Thus, we have investigated the mining of vertically partitioned datasets with possible overlap of attributes, which is the more likely case. Our experimental results have validated the efficiency of our proposed model achieving promising accuracy for real deployment.

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Using figures derived from the UK Home Office, this paper analyses and reviews the impact and deployment of Part V of the Criminal Justice and Public Order Act 1994 since its enactment. This is done with special reference to its impact on citizenship and the regulation of ‘the environment’ and associated rural spaces. It is argued that, notwithstanding the actual use of the public order clauses in Part V of the Act, its underlying meanings are largely of a symbolic nature. Such symbolism is, however, a powerful indication of the defence of particularist constructions of rural space. It can also open out new conditions of possibility, providing a useful ‘oppressed’ status and media spectacle for a range of protesters and activists.

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The effects and influence of the Building Research Establishment’s Environmental Assessment Methods (BREEAM) on construction professionals are examined. Most discussions of building assessment methods focus on either the formal tool or the finished product. In contrast, BREEAM is analysed here as a social technology using Michel Foucault’s theory of governmentality. Interview data are used to explore the effect of BREEAM on visibilities, knowledge, techniques and professional identities. The analysis highlights a number of features of the BREEAM assessment process which generally go unremarked: professional and public understandings of the method, the deployment of different types of knowledge and their implication for the authority and legitimacy of the tool, and the effect of BREEAM on standard practice. The analysis finds that BREEAM’s primary effect is through its impact on standard practices. Other effects include the use of assessment methods to defend design decisions, its role in both operationalizing and obscuring the concept of green buildings, and the effect of tensions between project and method requirements for the authority of the tool. A reflection on assessment methods as neo-liberal tools and their adequacy for the promotion of sustainable construction suggests several limitations of lock-in that hinder variation and wider systemic change.

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Enterprise Resource Planning is often endorsed as a means to facilitate strategic advantage for businesses. The scarcity of resources is the method by which some businesses maintain their position. However, the ubiquitous trend towards the adoption of Enterprise Resourcing Planning systems coupled with market saturation makes the promise of advantage less compelling. Reported in this paper is a proposed solution based upon semiotic theory that takes a typical Enterprise Resource Planning deployment scenario and shapes it according to the needs of people in post-implementation contexts to leverage strategic advantage in different ways.

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The deployment of genetic markers is of interest in crop assessment and breeding programmes, due to the potential savings in cost and time afforded. As part of the internationally recognised framework for the awarding of Plant Breeders’ Rights (PBR), new barley variety submissions are evaluated using a suite of morphological traits to ensure they are distinct, uniform and stable (DUS) in comparison to all previous submissions. Increasing knowledge of the genetic control of many of these traits provides the opportunity to assess the potential of deploying diagnostic/perfect genetic markers in place of phenotypic assessment. Here, we identify a suite of 25 genetic markers assaying for 14 DUS traits, and implement them using a single genotyping platform (KASPar). Using a panel of 169 UK barley varieties, we show that phenotypic state at three of these traits can be perfectly predicted by genotype. Predictive values for an additional nine traits ranged from 81 to 99 %. Finally, by comparison of varietal discrimination based on phenotype and genotype resulted in correlation of 0.72, indicating that deployment of molecular markers for varietal discrimination could be feasible in the near future. Due to the flexibility of the genotyping platform used, the genetic markers described here can be used in any number or combination, in-house or by outsourcing, allowing flexible deployment by users. These markers are likely to find application where tracking of specific alleles is required in breeding programmes, or for potential use within national assessment programmes for the awarding of PBRs.

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The growing human population will require a significant increase in agricultural production. This challenge is made more difficult by the fact that changes in the climatic and environmental conditions under which crops are grown have resulted in the appearance of new diseases, whereas genetic changes within the pathogen have resulted in the loss of previously effective sources of resistance. To help meet this challenge, advanced genetic and statistical methods of analysis have been used to identify new resistance genes through global screens, and studies of plant-pathogen interactions have been undertaken to uncover the mechanisms by which disease resistance is achieved. The informed deployment of major, race-specific and partial, race-nonspecific resistance, either by conventional breeding or transgenic approaches, will enable the production of crop varieties with effective resistance without impacting on other agronomically important crop traits. Here, we review these recent advances and progress towards the ultimate goal of developing disease-resistant crops.

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Purpose – The aim of this paper is to present a conceptual valuation framework to allow telecare service stakeholders to assess telecare devices in the home in terms of their social, psychological and practical effects. The framework enables telecare service operators to more effectively engage with the social and psychological issues resulting from telecare technology deployment in the home and to design and develop appropriate responses as a result. Design/methodology/approach – The paper provides a contextual background for the need for sociologically pitched tools that engage with the social and cultural feelings of telecare service users before presenting the valuation framework and how it could be used. Findings – A conceptual valuation framework is presented for potential development/use. Research limitations/implications – The valuation framework has yet to be extensively tested or verified. Practical implications – The valuation framework needs to be tested and deployed by a telecare service operator but the core messages of the paper are valid and interesting for readership. Social implications – In addressing the social and cultural perspectives of telecare service stakeholders, the paper makes a link between the technologies in the home, the feelings and orientations of service users (e.g. residents, emergency services, wardens, etc.) and the telecare service operator. Originality/value – The paper is an original contribution to the field as it details how the sociological orientations of telecare technology service users should be valued and addressed by service operators. It has a value through the conceptual arguments made and through valuation framework presented.

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What are the microfoundations of dynamic capabilities that sustain competitive advantage in a highly volatile environment, such as a transition economy? We explore the detailed nature of these dynamic capabilities along with their antecedents by tracing the sequence of their development based on a longitudinal case study of an organization subject to an external context of radical transition — the Russian oil company, Yukos. Our rich qualitative data indicate two distinct types of dynamic capabilities that are pivotal for organizational transformation. Adaptation dynamic capabilities relate to routines of resource exploitation and deployment, which are supported by acquisition, internalization and dissemination of extant knowledge, as well as resource reconfiguration, divestment and integration. Innovation dynamic capabilities relate to the creation of completely new capabilities via exploration and path-creation processes, which are supported by search, experimentation and risk taking, as well as project selection, funding and implementation. Second, we find that sequencing the two types of dynamic capabilities, helped the organization both to secure short-term competitive advantage, and to create the basis for long-term competitive advantage. These dynamic capability constructs advance theoretical understanding of what dynamic capabilities are, whilst their sequencing explains how firms create, leverage and enhance them over time.

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With a wide range of applications benefiting from dense network air temperature observations but with limitations of costs, existing siting guidelines and risk of damage to sensors, new methods are required to gain a high resolution understanding of the spatio-temporal patterns of urban meteorological phenomena such as the urban heat island or precision farming needs. With the launch of a new generation of low cost sensors it is possible to deploy a network to monitor air temperature at finer spatial resolutions. Here we investigate the Aginova Sentinel Micro (ASM) sensor with a bespoke radiation shield (together < US$150) which can provide secure near-real-time air temperature data to a server utilising existing (or user deployed) Wireless Fidelity (Wi-Fi) networks. This makes it ideally suited for deployment where wireless communications readily exist, notably urban areas. Assessment of the performance of the ASM relative to traceable standards in a water bath and atmospheric chamber show it to have good measurement accuracy with mean errors < ± 0.22 °C between -25 and 30 °C, with a time constant in ambient air of 110 ± 15 s. Subsequent field tests of it within the bespoke shield also had excellent performance (root-mean-square error = 0.13 °C) over a range of meteorological conditions relative to a traceable operational UK Met Office platinum resistance thermometer. These results indicate that the ASM and bespoke shield are more than fit-for-purpose for dense network deployment in urban areas at relatively low cost compared to existing observation techniques.

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Body Sensor Networks (BSNs) have been recently introduced for the remote monitoring of human activities in a broad range of application domains, such as health care, emergency management, fitness and behaviour surveillance. BSNs can be deployed in a community of people and can generate large amounts of contextual data that require a scalable approach for storage, processing and analysis. Cloud computing can provide a flexible storage and processing infrastructure to perform both online and offline analysis of data streams generated in BSNs. This paper proposes BodyCloud, a SaaS approach for community BSNs that supports the development and deployment of Cloud-assisted BSN applications. BodyCloud is a multi-tier application-level architecture that integrates a Cloud computing platform and BSN data streams middleware. BodyCloud provides programming abstractions that allow the rapid development of community BSN applications. This work describes the general architecture of the proposed approach and presents a case study for the real-time monitoring and analysis of cardiac data streams of many individuals.

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Body area networks (BANs) are emerging as enabling technology for many human-centered application domains such as health-care, sport, fitness, wellness, ergonomics, emergency, safety, security, and sociality. A BAN, which basically consists of wireless wearable sensor nodes usually coordinated by a static or mobile device, is mainly exploited to monitor single assisted livings. Data generated by a BAN can be processed in real-time by the BAN coordinator and/or transmitted to a server-side for online/offline processing and long-term storing. A network of BANs worn by a community of people produces large amount of contextual data that require a scalable and efficient approach for elaboration and storage. Cloud computing can provide a flexible storage and processing infrastructure to perform both online and offline analysis of body sensor data streams. In this paper, we motivate the introduction of Cloud-assisted BANs along with the main challenges that need to be addressed for their development and management. The current state-of-the-art is overviewed and framed according to the main requirements for effective Cloud-assisted BAN architectures. Finally, relevant open research issues in terms of efficiency, scalability, security, interoperability, prototyping, dynamic deployment and management, are discussed.

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Owing to continuous advances in the computational power of handheld devices like smartphones and tablet computers, it has become possible to perform Big Data operations including modern data mining processes onboard these small devices. A decade of research has proved the feasibility of what has been termed as Mobile Data Mining, with a focus on one mobile device running data mining processes. However, it is not before 2010 until the authors of this book initiated the Pocket Data Mining (PDM) project exploiting the seamless communication among handheld devices performing data analysis tasks that were infeasible until recently. PDM is the process of collaboratively extracting knowledge from distributed data streams in a mobile computing environment. This book provides the reader with an in-depth treatment on this emerging area of research. Details of techniques used and thorough experimental studies are given. More importantly and exclusive to this book, the authors provide detailed practical guide on the deployment of PDM in the mobile environment. An important extension to the basic implementation of PDM dealing with concept drift is also reported. In the era of Big Data, potential applications of paramount importance offered by PDM in a variety of domains including security, business and telemedicine are discussed.

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Reinforcing the Low Voltage (LV) distribution network will become essential to ensure it remains within its operating constraints as demand on the network increases. The deployment of energy storage in the distribution network provides an alternative to conventional reinforcement. This paper presents a control methodology for energy storage to reduce peak demand in a distribution network based on day-ahead demand forecasts and historical demand data. The control methodology pre-processes the forecast data prior to a planning phase to build in resilience to the inevitable errors between the forecasted and actual demand. The algorithm uses no real time adjustment so has an economical advantage over traditional storage control algorithms. Results show that peak demand on a single phase of a feeder can be reduced even when there are differences between the forecasted and the actual demand. In particular, results are presented that demonstrate when the algorithm is applied to a large number of single phase demand aggregations that it is possible to identify which of these aggregations are the most suitable candidates for the control methodology.

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There has been a recent surge in the use of silver as an antimicrobial agent in a wide range of domestic and clinical products, intended to prevent or treat bacterial infections and reduce bacterial colonization of surfaces. It has been reported that the antibacterial and cytotoxic properties of silver are affected by the assay conditions, particularly the type of growth media used in vitro. The toxicity of Ag+ to bacterial cells is comparable to that of human cells. We demonstrate that biologically relevant compounds such as glutathione, cysteine and human blood components significantly reduce the toxicity of silver ions to clinically relevant pathogenic bacteria and primary human dermal fibroblasts (skin cells). Bacteria are able to grow normally in the presence of silver nitrate at >20-fold the minimum inhibitory concentration (MIC) if Ag+ and thiols are added in a 1:1 ratio because the reaction of Ag+ with extracellular thiols prevents silver ions from interacting with cells. Extracellular thiols and human serum also significantly reduce the antimicrobial activity of silver wound dressings Aquacel-Ag (Convatec) and Acticoat (Smith & Nephew) to Staphylococcus aureus, Pseudomonas aeruginosa and Escherichia coli in vitro. These results have important implications for the deployment of silver as an antimicrobial agent in environments exposed to biological tissue or secretions. Significant amounts of money and effort have been directed at the development of silver-coated medical devices (e.g. dressings, catheters, implants). We believe our findings are essential for the effective design and testing of antimicrobial silver coatings.

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A flood warning system incorporates telemetered rainfall and flow/water level data measured at various locations in the catchment area. Real-time accurate data collection is required for this use, and sensor networks improve the system capabilities. However, existing sensor nodes struggle to satisfy the hydrological requirements in terms of autonomy, sensor hardware compatibility, reliability and long-range communication. We describe the design and development of a real-time measurement system for flood monitoring, and its deployment in a flash-flood prone 650 km2 semiarid watershed in Southern Spain. A developed low-power and long-range communication device, so-called DatalogV1, provides automatic data gathering and reliable transmission. DatalogV1 incorporates self-monitoring for adapting measurement schedules for consumption management and to capture events of interest. Two tests are used to assess the success of the development. The results show an autonomous and robust monitoring system for long-term collection of water level data in many sparse locations during flood events.