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Brockerhoff, Eckehard G. - One of the best experts on this subject based on the ideXlab platform.

  • Plant Pest Impact Metric System (PPIMS): Framework and guidelines for a common set of Metrics to classify and prioritise plant pests
    'Elsevier BV', 2020
    Co-Authors: Ireland, Kylie B., Van Klinken Rieks, Cook, David C., Logan David, Jamieson Lisa, Tyson, Joy L., Hulme, Philip E., Christopher Mandy, Worner Susan, Brockerhoff, Eckehard G.
    Abstract:

    Agricultural stakeholders need a common set of Metrics to evaluate plant pest impacts to facilitate transparency and harmonisation of pest management and prioritisation across spatial scales and jurisdictions. We propose a classification System that articulates, defines and classifies the magnitude of impacts (historical, current or potential) of pest species (alien and native) in plant production Systems. Metrics were identified and criteria defined through consideration of economic parameters, risk assessment standards and guidance tools, discussions with pest risk assessment practitioners and recent advances in environmental impact classification schemes. Twenty Metrics were identified and assigned to one of four key Metric types: spatiotemporal, market-driven, primary response and mid-to long-term response. Host crop value, Market access, Feasibility of management and Reversibility were identified as disruptor Metrics, likely to influence overall classification by at least twice that of other Metrics. Application of the System found it was able to classify well-known pests by importance, capturing changes in impact status as the management programme progressed for one pest, and how it was influenced by the geographic scale of assessment for another. Our work demonstrates the value of integrating plant protection science with invasion biology to derive a comprehensive measure of pest impact in agroecoSystems that can be utilised by all plant biosecurity stakeholders

Dominique Bourdet - One of the best experts on this subject based on the ideXlab platform.

  • well test analysis the use of advanced interpretation models
    2002
    Co-Authors: Dominique Bourdet
    Abstract:

    /homepage/sad/books/welltest/errata.html. Preface. Online Complement: /homepage/sad/books/welltest. 1. Principles of Transient Testing. 1.1 Introduction. 1.2 Typical flow regimes. 1.3 Well and reservoir characterization. 2. The Analysis Methods. 2.1 Log-log scale. 2.2 Pressure curves analysis. 2.3 Pressure derivative. 2.4 The analysis scales. 3. Wellbore Conditions. 3.1 Well with wellbore storage and skin. 3.2 Infinite conductivity or uniform flux vertical fracture. 3.3 Finite conductivity vertical fracture. 3.4 Well in partial penetration. 3.5 Slanted well. 3.6 Horizontal well. 3.7 Skin factors. 4. Effect of Reservoir Heterogeneities on Well Responses. 4.1 Fissured reservoirs. 4.2 Layered reservoirs with or without crossflow. 4.3 Composite reservoirs. 4.4 Combined reservoir heterogeneities. 5. Effect of Reservoir Boundaries on Well Responses. 5.1 Single sealing fault in a homogeneous reservoir. 5.2 Two parallel sealing faults in homogeneous reservoir. 5.3 Two intersecting sealing faults in homogeneous reservoir. 5.4 Closed homogeneous reservoir. 5.5 Constant pressure boundary. 5.6 Communicating fault. 5.7 Effect of boundaries in double porosity reservoirs. 5.8 Effect of boundaries in double permeability reservoirs. 5.9 Effect of boundaries in composite reservoirs. 5.10 Other boundary configurations. 5.11 Conclusion. 6. Multiple Well Testing. 6.1 Interference tests in reservoirs with homogeneous behavior. 6.2 Factors complicating interference tests in reservoirs with homogeneous behavior. 6.3 Interference tests in composite reservoirs. 6.4 Interference tests in double porosity reservoirs. 6.5 Interference tests in layered reservoirs. 6.6 Pulse testing. 6.7 Conclusion. 7. Application to Gas Reservoirs. 7.1 Description of gas wells pressure behavior. 7.2 Practical transient analysis of gas welltests. 7.3 Deliverability tests. 7.4 Field example. 8. Application to Multiphase Reservoirs. 8.1 Perrine's method. 8.2 Pseudo-pressure method. 8.3 Pressure squared method. 9. Special Tests. 9.1 DST. 9.2 Impulse test. 9.3 Constant pressure test, and rate decline analysis. 9.4 Vertical interference test. 10. Practical Aspects of Well Test Interpretation. 10.1 Factors complicating well test analysis. 10.2 Interpretation procedure. 10.3 Well and reservoir characterisation- interpretation results. Appendix 1. Summary of Usual Log-Log Responses. Appendix 2. Practical Metric System of Units. Nomenclature. References. Author Index. Subject Index.

  • well test analysis the use of advanced interpretation models
    2002
    Co-Authors: Dominique Bourdet
    Abstract:

    /homepage/sad/books/welltest/errata.html. Preface. Online Complement: /homepage/sad/books/welltest. 1. Principles of Transient Testing. 1.1 Introduction. 1.2 Typical flow regimes. 1.3 Well and reservoir characterization. 2. The Analysis Methods. 2.1 Log-log scale. 2.2 Pressure curves analysis. 2.3 Pressure derivative. 2.4 The analysis scales. 3. Wellbore Conditions. 3.1 Well with wellbore storage and skin. 3.2 Infinite conductivity or uniform flux vertical fracture. 3.3 Finite conductivity vertical fracture. 3.4 Well in partial penetration. 3.5 Slanted well. 3.6 Horizontal well. 3.7 Skin factors. 4. Effect of Reservoir Heterogeneities on Well Responses. 4.1 Fissured reservoirs. 4.2 Layered reservoirs with or without crossflow. 4.3 Composite reservoirs. 4.4 Combined reservoir heterogeneities. 5. Effect of Reservoir Boundaries on Well Responses. 5.1 Single sealing fault in a homogeneous reservoir. 5.2 Two parallel sealing faults in homogeneous reservoir. 5.3 Two intersecting sealing faults in homogeneous reservoir. 5.4 Closed homogeneous reservoir. 5.5 Constant pressure boundary. 5.6 Communicating fault. 5.7 Effect of boundaries in double porosity reservoirs. 5.8 Effect of boundaries in double permeability reservoirs. 5.9 Effect of boundaries in composite reservoirs. 5.10 Other boundary configurations. 5.11 Conclusion. 6. Multiple Well Testing. 6.1 Interference tests in reservoirs with homogeneous behavior. 6.2 Factors complicating interference tests in reservoirs with homogeneous behavior. 6.3 Interference tests in composite reservoirs. 6.4 Interference tests in double porosity reservoirs. 6.5 Interference tests in layered reservoirs. 6.6 Pulse testing. 6.7 Conclusion. 7. Application to Gas Reservoirs. 7.1 Description of gas wells pressure behavior. 7.2 Practical transient analysis of gas welltests. 7.3 Deliverability tests. 7.4 Field example. 8. Application to Multiphase Reservoirs. 8.1 Perrine's method. 8.2 Pseudo-pressure method. 8.3 Pressure squared method. 9. Special Tests. 9.1 DST. 9.2 Impulse test. 9.3 Constant pressure test, and rate decline analysis. 9.4 Vertical interference test. 10. Practical Aspects of Well Test Interpretation. 10.1 Factors complicating well test analysis. 10.2 Interpretation procedure. 10.3 Well and reservoir characterisation- interpretation results. Appendix 1. Summary of Usual Log-Log Responses. Appendix 2. Practical Metric System of Units. Nomenclature. References. Author Index. Subject Index.

Priyadarshan Kolte - One of the best experts on this subject based on the ideXlab platform.

  • a software Metric System for module coupling
    Journal of Systems and Software, 1993
    Co-Authors: Jefferson A Offutt, Mary Jean Harrold, Priyadarshan Kolte
    Abstract:

    Abstract Low module coupling is considered to be a desirable quality for modular programs to have. Previously, coupling has been defined subjectively and not quantified, making it difficult to use in practice. In this article, we extend previous work to reflect newer programming languages and quantify coupling by developing a general software Metric System that allows us to automatically measure coupling. We have precisely defined the levels of coupling so that they can be determined algorithmically, incorporated the notion of direction into the coupling levels, and accounted for different types of nonlocal variables present in modern programming languages. With our System, we can measure the coupling between all pairs of modules in a System, measure the coupling of a particular module with all other modules in a System, and measure the coupling of an entire System. We have implemented our Metric System so that it measures the coupling between pairs of procedures in arbitrary C programs and have analyzed several well-used Systems of various sizes.

Ireland, Kylie B. - One of the best experts on this subject based on the ideXlab platform.

  • Plant Pest Impact Metric System (PPIMS): Framework and guidelines for a common set of Metrics to classify and prioritise plant pests
    'Elsevier BV', 2020
    Co-Authors: Ireland, Kylie B., Van Klinken Rieks, Cook, David C., Logan David, Jamieson Lisa, Tyson, Joy L., Hulme, Philip E., Christopher Mandy, Worner Susan, Brockerhoff, Eckehard G.
    Abstract:

    Agricultural stakeholders need a common set of Metrics to evaluate plant pest impacts to facilitate transparency and harmonisation of pest management and prioritisation across spatial scales and jurisdictions. We propose a classification System that articulates, defines and classifies the magnitude of impacts (historical, current or potential) of pest species (alien and native) in plant production Systems. Metrics were identified and criteria defined through consideration of economic parameters, risk assessment standards and guidance tools, discussions with pest risk assessment practitioners and recent advances in environmental impact classification schemes. Twenty Metrics were identified and assigned to one of four key Metric types: spatiotemporal, market-driven, primary response and mid-to long-term response. Host crop value, Market access, Feasibility of management and Reversibility were identified as disruptor Metrics, likely to influence overall classification by at least twice that of other Metrics. Application of the System found it was able to classify well-known pests by importance, capturing changes in impact status as the management programme progressed for one pest, and how it was influenced by the geographic scale of assessment for another. Our work demonstrates the value of integrating plant protection science with invasion biology to derive a comprehensive measure of pest impact in agroecoSystems that can be utilised by all plant biosecurity stakeholders

Mark J Ratain - One of the best experts on this subject based on the ideXlab platform.

  • time to tumor growth a model end point and new Metric System for oncology clinical trials
    Journal of Clinical Oncology, 2013
    Co-Authors: Michael L Maitland, Lawrence H Schwartz, Mark J Ratain
    Abstract:

    There has been extensive societal investment in oncology drug discovery and development during the past decade. However, there has been limited innovation in clinical trial design during the same period, both in the private sector and in government-sponsored clinical trials, especially with regard to end point evaluation. Innovations in clinical trial design could accelerate availability of effective new drugs and reduce the rate of failure in expensive late-phase development, and therefore reduce the overall costs of oncology drug development. Although there have been some changes, such as the increased use of progression-free survival (PFS) as a primary end point for phase II trials, almost all trials (including those with PFS end points) continue to use the RECIST criteria, which distinguish progressive disease or partial response from stable disease by arbitrary dividing lines, rather than using the actual richness of the carefully collected radiologic data. PFS is of particular interest as a surrogate end point for phase III trials, as exemplified by recent meta-analyses in metastatic colorectal cancer. But the operating characteristics of studies with a PFS end point have not been fully characterized, their design has yet to be optimized, and improvements in PFS do not necessarily predict improvements in overall survival in all solid tumors. As more effective treatment strategies for colorectal cancer evolve, the association between PFS and overall survival will likely decline. Alternative Metrics to PFS for detecting beneficial effects of novel cancer therapies have become an active area of research. Metrics that require shorter periods of observation or provide more precise assessment of treatment effects than PFS could lead to more rapid completion of clinical trials and require fewer patients. These alternative Metrics might also better quantify heterogeneous patterns of human tumor growth and treatment response. In turn, investigators could better delineate these patterns in cancer patients and more quickly discover biomarkers that improve therapy. In the article that accompanies this editorial, Claret et al identified time to tumor growth (TTG) as the best Metric to predict overall survival in metastatic colorectal cancer patients treated in the first-line setting with fluorouracil, leucovorin, and irinotecan with or without added bevacizumab. This is an intuitive end point, in that a delay in tumor growth would be expected to improve survival. However, unlike PFS (or its related Metric, time to progression), TTG captures the change in the growth curve, rather than determining the time to pass the magic line of RECISTdefined progressive disease. Whereas patients may have long PFS as a result of indolent disease, if the tumor is continuously growing, TTG would be quite short. Thus, TTG allows differentiation of drug effect from a favorable prognosis. The current effort builds on the authors’ prior modeling efforts comparing several Metrics of treatment effects derived from computed tomography (CT) imaging data collected in two phase III trials. The Metrics they evaluated are fully described and clearly displayed visually in Figure 1 of their paper and include the changes in the sum of the longest diameter measures of target lesions after 8 weeks of therapy—the tumor size (TS) ratio; an empirically derived tumor growth rate constant “g”; and TTG. For reference, the associations of these Metrics with overall survival in the phase III trials were compared with best response rate (the fraction of patients who had decrease in total measured tumor burden greater than 30%) and PFS (the median time to which patients survive before the total measured tumor burden has increased by 20% from the minimum measured tumor burden during treatment) by RECIST. In first-line metastatic colorectal cancer treated with combined cytotoxic and bevacizumab therapy, the model incorporating the TTG parameter most reliably predicted the ultimate improvement in overall survival reflected by the hazard ratio for bevacizumab-treated patients. As the authors note in their discussion, this is not yet a completely validated end point, and TTG will certainly be evaluated in similar data sets before it becomes commonly used as an end point for new clinical trials. However, the results are sufficiently compelling that sponsors and investigators who are currently evaluating novel agents in first-line colorectal cancer should analyze their data for the TTG end point and consider the likely projected outcome of a simulated phase III trial before deciding on how to design and whether to proceed to a (or even complete an ongoing) phase III trial. If TTG proves a robust end point in this clinical setting, one advantage will be the reduced follow-up time needed for each patient on trial, and consequently, trials with this end point would likely have reduced costs and reach their conclusions sooner than more conventional PFS-based studies. The investigators assert that TTG in the colorectal cancer model has an advantage of being treatment independent. However, it is unclear how well TTG can be generalized to other treatment settings. JOURNAL OF CLINICAL ONCOLOGY E D I T O R I A L VOLUME 31 NUMBER 17 JUNE 1