904 resultados para Relocation reuse


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Multiobjective fuzzy methodology is applied to a case study of Khadakwasla complex irrigation project located near Pune city of Maharashtra State, India. Three objectives, namely, maximization of net benefits, crop production and labour employment are considered. Effect of reuse of wastewater on the planning scenario is also studied. Three membership functions, namely, nonlinear, hyperbolic and exponential are analyzed for multiobjective fuzzy optimization. In the present study, objective functions are considered as fuzzy in nature whereas inflows are considered as dependable. It is concluded that exponential and hyperbolic membership functions provided similar cropping pattern for most of the situations whereas nonlinear membership functions provided different cropping pattern. However, in all the three cases, irrigation intensities are more than the existing irrigation intensity.

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Multi-GPU machines are being increasingly used in high-performance computing. Each GPU in such a machine has its own memory and does not share the address space either with the host CPU or other GPUs. Hence, applications utilizing multiple GPUs have to manually allocate and manage data on each GPU. Existing works that propose to automate data allocations for GPUs have limitations and inefficiencies in terms of allocation sizes, exploiting reuse, transfer costs, and scalability. We propose a scalable and fully automatic data allocation and buffer management scheme for affine loop nests on multi-GPU machines. We call it the Bounding-Box-based Memory Manager (BBMM). BBMM can perform at runtime, during standard set operations like union, intersection, and difference, finding subset and superset relations on hyperrectangular regions of array data (bounding boxes). It uses these operations along with some compiler assistance to identify, allocate, and manage data required by applications in terms of disjoint bounding boxes. This allows it to (1) allocate exactly or nearly as much data as is required by computations running on each GPU, (2) efficiently track buffer allocations and hence maximize data reuse across tiles and minimize data transfer overhead, and (3) and as a result, maximize utilization of the combined memory on multi-GPU machines. BBMM can work with any choice of parallelizing transformations, computation placement, and scheduling schemes, whether static or dynamic. Experiments run on a four-GPU machine with various scientific programs showed that BBMM reduces data allocations on each GPU by up to 75% compared to current allocation schemes, yields performance of at least 88% of manually written code, and allows excellent weak scaling.

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Gray water treatment and reuse is an immediate option to counter the upcoming water shortages in various parts of world, especially urban areas. Anaerobic treatment of gray water in houses is an alternative low cost, low energy and low sludge generating option that can meet this challenge. Typical problems of fluctuating VFA, low pH and sludge washout at low loading rates with gray water feedstock was overcome in two chambered anaerobic biofilm reactors using natural fibers as the biofilm support. The long term performance of using natural fiber based biofilms at moderate and low organic loading rates (OLR) have been examined. Biofilms raised on natural fibers (coir, ridge-gourd) were similar to that of synthetic media (PVC, polyethylene) at lower OLR when operated in pulse fed mode without effluent recirculation and achieved 80-90% COD removal at HRT of 2 d showing a small variability during start-up. Confocal microscopy of the biofilms on natural fibers indicated thinner biofilms, dense cell architecture and low extra cellular polymeric substances (EPS) compared to synthetic supports and this is believed to be key factor in high performance at low OLR and low strength gray water. Natural fibers are thus shown to be an effective biofilm support that withstand fluctuating characteristic of domestic gray water. (C) 2013 The Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

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In this paper we present HyperCell as a reconfigurable datapath for Instruction Extensions (IEs). HyperCell comprises an array of compute units laid over a switch network. We present an IE synthesis methodology that enables post-silicon realization of IE datapaths on HyperCell. The synthesis methodology optimally exploits hardware resources in HyperCell to enable software pipelined execution of IEs. Exploitation of temporal reuse of data in HyperCell results in significant reduction of input/output bandwidth requirements of HyperCell.

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The broader goal of the research being described here is to automatically acquire diagnostic knowledge from documents in the domain of manual and mechanical assembly of aircraft structures. These documents are treated as a discourse used by experts to communicate with others. It therefore becomes possible to use discourse analysis to enable machine understanding of the text. The research challenge addressed in the paper is to identify documents or sections of documents that are potential sources of knowledge. In a subsequent step, domain knowledge will be extracted from these segments. The segmentation task requires partitioning the document into relevant segments and understanding the context of each segment. In discourse analysis, the division of a discourse into various segments is achieved through certain indicative clauses called cue phrases that indicate changes in the discourse context. However, in formal documents such language may not be used. Hence the use of a domain specific ontology and an assembly process model is proposed to segregate chunks of the text based on a local context. Elements of the ontology/model, and their related terms serve as indicators of current context for a segment and changes in context between segments. Local contexts are aggregated for increasingly larger segments to identify if the document (or portions of it) pertains to the topic of interest, namely, assembly. Knowledge acquired through such processes enables acquisition and reuse of knowledge during any part of the lifecycle of a product.

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An in situ study of stress evolution and mechanical behavior of germanium as a lithium-ion battery electrode material is presented. Thin films of germanium are cycled in a half-cell configuration with lithium metal foil as counter/reference electrode, with 1M LiPF6 in ethylene carbonate, diethyl carbonate, dimethyl carbonate solution (1:1:1, wt%) as electrolyte. Real-time stress evolution in the germanium thin-film electrodes during electrochemical lithiation/delithiation is measured by monitoring the substrate curvature using the multi-beam optical sensing method. Upon lithiation a-Ge undergoes extensive plastic deformation, with a peak compressive stress reaching as high as -0.76 +/- 0.05 GPa (mean +/- standard deviation). The compressive stress decreases with lithium concentration reaching a value of approximately -0.3 GPa at the end of lithiation. Upon delithiation the stress quickly became tensile and follows a trend that mirrors the behavior on compressive side; the average peak tensile stress of the lithiated Ge samples was approximately 0.83 GPa. The peak tensile stress data along with the SEM analysis was used to estimate a lower bound fracture resistance of lithiated Ge, which is approximately 5.3 J/m(2). It was also observed that the lithiated Ge is rate sensitive, i.e., stress depends on how fast or slow the charging is carried out. (C) The Author(s) 2015. Published by ECS. This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 License (CC BY, http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse of the work in any medium, provided the original work is properly cited. All rights reserved.

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The polyhedral model provides an expressive intermediate representation that is convenient for the analysis and subsequent transformation of affine loop nests. Several heuristics exist for achieving complex program transformations in this model. However, there is also considerable scope to utilize this model to tackle the problem of automatic memory footprint optimization. In this paper, we present a new automatic storage optimization technique which can be used to achieve both intra-array as well as inter-array storage reuse with a pre-determined schedule for the computation. Our approach works by finding statement-wise storage partitioning hyper planes that partition a unified global array space so that values with overlapping live ranges are not mapped to the same partition. Our heuristic is driven by a fourfold objective function which not only minimizes the dimensionality and storage requirements of arrays required for each high-level statement, but also maximizes inter statement storage reuse. The storage mappings obtained using our heuristic can be asymptotically better than those obtained by any existing technique. We implement our technique and demonstrate its practical impact by evaluating its effectiveness on several benchmarks chosen from the domains of image processing, stencil computations, and high-performance computing.

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A desorganização de dados em bancos de dados automatizados implica em ineficiência operacional causada por redundâncias, inconsistências, baixo reuso, riscos informacionais, enfim, menor valor agregado da TI aos objetivos organizacionais. Apoiado por técnicas de gerenciamento de projetos, busca-se em um estudo de caso na Câmara dos Deputados a otimização da organização dos dados em bancos de dados automatizados.

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Reuse is at the heart of major improvements in productivity and quality in Software Engineering. Both Model Driven Engineering (MDE) and Software Product Line Engineering (SPLE) are software development paradigms that promote reuse. Specifically, they promote systematic reuse and a departure from craftsmanship towards an industrialization of the software development process. MDE and SPLE have established their benefits separately. Their combination, here called Model Driven Product Line Engineering (MDPLE), gathers together the advantages of both. Nevertheless, this blending requires MDE to be recasted in SPLE terms. This has implications on both the core assets and the software development process. The challenges are twofold: (i) models become central core assets from which products are obtained and (ii) the software development process needs to cater for the changes that SPLE and MDE introduce. This dissertation proposes a solution to the first challenge following a feature oriented approach, with an emphasis on reuse and early detection of inconsistencies. The second part is dedicated to assembly processes, a clear example of the complexity MDPLE introduces in software development processes. This work advocates for a new discipline inside the general software development process, i.e., the Assembly Plan Management, which raises the abstraction level and increases reuse in such processes. Different case studies illustrate the presented ideas.

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CONTENTS: Learning insights from the Fisheries Resource Management Project, by Tee-Jay A San Diego. An orientation on the SIAD approach and participatory local development planning, by Elizabeth M. Gonzales. Group-building, production success and the struggle to prevent capture of the resource, by B.K. Sahay, K.P. Singh and S.N. Pandeya. Urban agriculture, water reuse and local economies: case study of coastal riverine Settlements of Ondo State, Nigeria, by Yemi Akegbejo-Samsons. Livelihoods analysis: actual experience from using PRA, by Pham Minh Tam and Trinh Quang Tu. A sustainable livelihoods approach to fisheries development for poverty alleviation in southeastern Vietnam, by Nguyen Van Tu and Nguyen Minh Duc

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In a time when Technology Supported Learning Systems are being widely used, there is a lack of tools that allows their development in an automatic or semi-automatic way. Technology Supported Learning Systems require an appropriate Domain Module, ie. the pedagogical representation of the domain to be mastered, in order to be effective. However, content authoring is a time and effort consuming task, therefore, efforts in automatising the Domain Module acquisition are necessary.Traditionally, textbooks have been used as the main mechanism to maintain and transmit the knowledge of a certain subject or domain. Textbooks have been authored by domain experts who have organised the contents in a means that facilitate understanding and learning, considering pedagogical issues.Given that textbooks are appropriate sources of information, they can be used to facilitate the development of the Domain Module allowing the identification of the topics to be mastered and the pedagogical relationships among them, as well as the extraction of Learning Objects, ie. meaningful fragments of the textbook with educational purpose.Consequently, in this work DOM-Sortze, a framework for the semi-automatic construction of Domain Modules from electronic textbooks, has been developed. DOM-Sortze uses NLP techniques, heuristic reasoning and ontologies to fulfill its work. DOM-Sortze has been designed and developed with the aim of automatising the development of the Domain Module, regardless of the subject, promoting the knowledge reuse and facilitating the collaboration of the users during the process.

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Workshop Research Data Management – Activities and Challenges 14-15 November 2011, Bonn The Knowledge Exchange initiative organised a workshop to highlight current activities and challenges with respect to research data management in the Knowledge Exchange partner countries and beyond. The workshop brought together experts from data centres, libraries, computational centres, funding organisations, publishing services and other institutions in the field of research and higher education who are working to improve research data management and encourage effective reuse of research data. A considerable part of the programme was dedicated to sharing perspectives from these communities, leading to the development of a roadmap of practical actions for the Knowledge Exchange initiative, partner organisations and other stakeholders to progress over the next two years. On the first day, principal investigators and project managers from a great variety of recent projects shared their insights on objectives and methods for improving data management ranging from discipline-specific to more general approaches. A series of short presentations of selected projects was followed by an extensive poster session that functioned as a “trade fair” of current trends and activities in the field of research data management. Moreover, the poster session offered ample network opportunities for participants. The second day was dedicated to intensive group discussions looking at a number of data management challenges. First the most important findings from the "Surfboard for 'Riding the Wave'" report were presented. This included the state of the art on activities and challenges in the field of research data management. The subgroups will concentrate on the following key themes: funding, incentives, training and technical infrastructure. These discussions culminated in the identification of practical recommendations for future cooperation on practical as well as on strategic levels that should be taken forward by the KE partner organisations and beyond. These activities aim to improve the sustainability of services and infrastructures at both national and international levels.