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  • 11
    Electronic Resource
    Electronic Resource
    Exporting new public management: performance agreements in a Pacific microstate (2005)
    Bingley : Emerald
    International journal of public sector management 18 (2005), S. 615-628 
    Details
    ISSN: 0951-3558
    Source: Emerald Fulltext Archive Database 1994-2005
    Topics: Political Science , Economics
    Notes: Purpose - The purpose of this article is to explore the export of new public management (NPM) to developing countries and to describe and evaluate the introduction of these initiatives in very different environments from their origins. Design/methodology/approach - The article traces the introduction of performance agreements into the public service of Vanuatu. Performance agreements are identified as an initiative typically promoted by NPM. The Vanuatu case is set within a review of the origin, use and record of performance agreements in countries such as Australia, the UK and the USA. Findings - The adoption of performance agreements has been slow and has enjoyed limited success. Among the difficulties encountered are suspicion, lack of incentives, an unreceptive environment, and possible identification as being donor-driven. It is difficult to see performance agreements in their current form making an impact on performance improvement in the Vanuatu public service. Practical implications - NPM initiatives must be carefully considered before being transferred to other countries. They may offer benefits but what has worked in one environment will often need considerable modification, certain preconditions and lengthy lead-in time to be effective in another environment. Originality/value - There are few case studies of attempts to transfer NPM-style reforms to developing countries and none on performance agreements, yet many countries in the Pacific and elsewhere are becoming interested in this mode of performance management. This case study helps to fill this gap through description and analysis of the Vanuatu experience and provides practical lessons for others considering policy transfer of NPM initiatives such as performance agreements.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1108/09513550510624077
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    Paper (German National Licenses)
    Fulltext
  • 12
    Electronic Resource
    Electronic Resource
    Trafficking of wheat gluten proteins in transgenic tobacco plants: γ-gliadin does not contain an endoplasmic reticulum-retention signal (1997)
    Springer
    Planta 203 (1997), S. 488-494 
    Details
    ISSN: 1432-2048
    Keywords: Key words: Endoplasmic reticulum (retention) ; γ-Gliadin ; Nicotiana ; Vacuolar targeting
    Source: Springer Online Journal Archives 1860-2000
    Topics: Biology
    Notes: Abstract. Wild-type and mutated forms of the wheat (Triticum aestivum L.) storage protein γ-gliadin were expressed in transgenic tobacco (Nicotiana tabacum L. cv. NVS) under the control of the 35S cauliflower mosaic virus (CaMV) promoter in order to determine what, if any, endogenous targeting signals are present in the wild-type γ-gliadin protein. The mutant forms of the protein were modified by the addition of a KDEL or HDEL C-terminal endoplasmic reticulum-retention signal, or the addition of a C-terminal propeptide from barley lectin which has been shown to be necessary and sufficient for targeting to the vacuole. Only modified forms of the protein accumulated in leaves of transgenic tobacco, although the transcript levels were similar for all the constructs. Pulse-chase analysis indicated that whereas the wild-type γ-gliadin was rapidly turned over in tobacco leaves, KDEL and HDEL forms were highly stable. The vacuolar-signal mutant protein accumulated in tobacco leaves, but migrated on sodium dodecyl sulphate-polyacrylamide gel electrophoresis with a lower mobility than wild-type γ-gliadin, due in part to glycosylation of the C-terminal propeptide. The vacuolar-signal mutant protein was turned over slowly in tobacco, perhaps indicating a poor level of transport competence. When pulse-chase analysis was carried out on protoplasts isolated from tobacco plants expressing wild-type γ-gliadin, but in the presence of Brefeldin A, γ-gliadin was seen to accumulate. Taken together, these results indicate that γ-gliadin is targeted to the vacuole in transgenic tobacco plants and does not contain any structural determinants which confer retention in the endoplasmic reticulum.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/s004250050218
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    Paper (German National Licenses)
    Fulltext
  • 13
    Electronic Resource
    Electronic Resource
    Statistical graphics in geochemical journals (1987)
    Springer
    Mathematical geology 19 (1987), S. 1-24 
    Details
    ISSN: 1573-8868
    Keywords: mathematical geology ; statisics ; statistical graphics ; geochemistry
    Source: Springer Online Journal Archives 1860-2000
    Topics: Geosciences , Mathematics
    Notes: Abstract A quantitative investigation of types of statistical graphics and maps being used for presentation of geochemical data has been made for the 1984 volumes ofGeochemica et Cosmochimica Acta, Chemical Geology, Contributions to Mineralogy and Petrology, Geochemistry International, Journal of Geochemical Exploration, Journal of Petrology, and the 1983 volume ofOrganic Geochemistry. Although a number of significant differences exist between relative proportions of text, tables, statistical graphs and maps, and other illustrations in the various journals, overall figures are comparable broadly to those found by Cleveland (1984) in a recent survey of usage in 57 journals in natural, mathematical, and social sciences. Between 18% and 35% of 1463 graphs and maps in these journals contain at least one item of error or poor presentation, indicating the need for substantial improvement in both standards of preparation and refereeing of diagrams, and we suggest guidelines for both authors and referees.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01275431
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    Paper (German National Licenses)
    Fulltext
  • 14
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    Unknown
    Adaptive Cut Selection in Mixed-Integer Linear Programming (2022)
    Details
    Publication Date: 2022-08-08
    Description: Cut selection is a subroutine used in all modern mixed-integer linear programming solvers with the goal of selecting a subset of generated cuts that induce optimal solver performance. These solvers have millions of parameter combinations, and so are excellent candidates for parameter tuning. Cut selection scoring rules are usually weighted sums of different measurements, where the weights are parameters. We present a parametric family of mixed-integer linear programs together with infinitely many family-wide valid cuts. Some of these cuts can induce integer optimal solutions directly after being applied, while others fail to do so even if an infinite amount are applied. We show for a specific cut selection rule, that any finite grid search of the parameter space will always miss all parameter values, which select integer optimal inducing cuts in an infinite amount of our problems. We propose a variation on the design of existing graph convolutional neural networks, adapting them to learn cut selection rule parameters. We present a reinforcement learning framework for selecting cuts, and train our design using said framework over MIPLIB 2017. Our framework and design show that adaptive cut selection does substantially improve performance over a diverse set of instances, but that finding a single function describing such a rule is difficult. Code for reproducing all experiments is available at https://github.com/Opt-Mucca/Adaptive-Cutsel-MILP.
    Language: English
    Type: reportzib , doc-type:preprint
    Format: application/pdf
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    OPUS
  • 15
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    Unknown
    Generative deep learning for decision making in gas networks (2022)
    Details
    Publication Date: 2023-07-14
    Description: A decision support system relies on frequent re-solving of similar problem instances. While the general structure remains the same in corresponding applications, the input parameters are updated on a regular basis. We propose a generative neural network design for learning integer decision variables of mixed-integer linear programming (MILP) formulations of these problems. We utilise a deep neural network discriminator and a MILP solver as our oracle to train our generative neural network. In this article, we present the results of our design applied to the transient gas optimisation problem. With the trained network we produce a feasible solution in 2.5s, use it as a warm-start solution, and thereby decrease global optimal solution solve time by 60.5%.
    Language: English
    Type: article , doc-type:article
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    OPUS
  • 16
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    Unknown
    Controlling transient gas flow in real-world pipeline intersection areas (2021)
    Details
    Publication Date: 2023-07-14
    Description: Compressor stations are the heart of every high-pressure gas transport network. Located at intersection areas of the network they are contained in huge complex plants, where they are in combination with valves and regulators responsible for routing and pushing the gas through the network. Due to their complexity and lack of data compressor stations are usually dealt with in the scientific literature in a highly simplified and idealized manner. As part of an ongoing project with one of Germany's largest Transmission System Operators to develop a decision support system for their dispatching center, we investigated how to automatize control of compressor stations. Each station has to be in a particular configuration, leading in combination with the other nearby elements to a discrete set of up to 2000 possible feasible operation modes in the intersection area. Since the desired performance of the station changes over time, the configuration of the station has to adapt. Our goal is to minimize the necessary changes in the overall operation modes and related elements over time, while fulfilling a preset performance envelope or demand scenario. This article describes the chosen model and the implemented mixed integer programming based algorithms to tackle this challenge. By presenting extensive computational results on real world data we demonstrate the performance of our approach.
    Language: English
    Type: article , doc-type:article
    Library Location Call Number Volume/Issue/Year Availability
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    OPUS
  • 17
    facet.materialart.
    Unknown
    Generative deep learning for decision making in gas networks (2020)
    Details
    Publication Date: 2023-07-14
    Description: A decision support system relies on frequent re-solving of similar problem instances. While the general structure remains the same in corresponding applications, the input parameters are updated on a regular basis. We propose a generative neural network design for learning integer decision variables of mixed-integer linear programming (MILP) formulations of these problems. We utilise a deep neural network discriminator and a MILP solver as our oracle to train our generative neural network. In this article, we present the results of our design applied to the transient gas optimisation problem. With the trained network we produce a feasible solution in 2.5s, use it as a warm-start solution, and thereby decrease global optimal solution solve time by 60.5%.
    Language: English
    Type: reportzib , doc-type:preprint
    Format: application/pdf
    Format: application/pdf
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    OPUS
  • 18
    facet.materialart.
    Unknown
    Controlling transient gas flow in real-world pipeline intersection areas (2019)
    Details
    Publication Date: 2023-07-14
    Description: Compressor stations are the heart of every high-pressure gas transport network. Located at intersection areas of the network they are contained in huge complex plants, where they are in combination with valves and regulators responsible for routing and pushing the gas through the network. Due to their complexity and lack of data compressor stations are usually dealt with in the scientific literature in a highly simplified and idealized manner. As part of an ongoing project with one of Germany's largest Transmission System Operators to develop a decision support system for their dispatching center, we investigated how to automatize control of compressor stations. Each station has to be in a particular configuration, leading in combination with the other nearby elements to a discrete set of up to 2000 possible feasible operation modes in the intersection area. Since the desired performance of the station changes over time, the configuration of the station has to adapt. Our goal is to minimize the necessary changes in the overall operation modes and related elements over time, while fulfilling a preset performance envelope or demand scenario. This article describes the chosen model and the implemented mixed integer programming based algorithms to tackle this challenge. By presenting extensive computational results on real world data we demonstrate the performance of our approach.
    Language: English
    Type: reportzib , doc-type:preprint
    Format: application/pdf
    Format: application/pdf
    Format: application/pdf
    Library Location Call Number Volume/Issue/Year Availability
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    OPUS
  • 19
    facet.materialart.
    Unknown
    Adaptive Cut Selection in Mixed-Integer Linear Programming (2023)
    Details
    Publication Date: 2023-08-02
    Description: Cutting plane selection is a subroutine used in all modern mixed-integer linear programming solvers with the goal of selecting a subset of generated cuts that induce optimal solver performance. These solvers have millions of parameter combinations, and so are excellent candidates for parameter tuning. Cut selection scoring rules are usually weighted sums of different measurements, where the weights are parameters. We present a parametric family of mixed-integer linear programs together with infinitely many family-wide valid cuts. Some of these cuts can induce integer optimal solutions directly after being applied, while others fail to do so even if an infinite amount are applied. We show for a specific cut selection rule, that any finite grid search of the parameter space will always miss all parameter values, which select integer optimal inducing cuts in an infinite amount of our problems. We propose a variation on the design of existing graph convolutional neural networks, adapting them to learn cut selection rule parameters. We present a reinforcement learning framework for selecting cuts, and train our design using said framework over MIPLIB 2017 and a neural network verification data set. Our framework and design show that adaptive cut selection does substantially improve performance over a diverse set of instances, but that finding a single function describing such a rule is difficult. Code for reproducing all experiments is available at https://github.com/Opt-Mucca/Adaptive-Cutsel-MILP.
    Language: English
    Type: article , doc-type:article
    Library Location Call Number Volume/Issue/Year Availability
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    OPUS
  • 20
    facet.materialart.
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    PySCIPOpt-ML: Embedding Trained Machine Learning Models into Mixed-Integer Programs (2023)
    Details
    Publication Date: 2023-12-20
    Description: A standard tool for modelling real-world optimisation problems is mixed-integer programming (MIP). However, for many of these problems there is either incomplete information describing variable relations, or the relations between variables are highly complex. To overcome both these hurdles, machine learning (ML) models are often used and embedded in the MIP as surrogate models to represent these relations. Due to the large amount of available ML frameworks, formulating ML models into MIPs is highly non-trivial. In this paper we propose a tool for the automatic MIP formulation of trained ML models, allowing easy integration of ML constraints into MIPs. In addition, we introduce a library of MIP instances with embedded ML constraints. The project is available at https://github.com/Opt-Mucca/PySCIPOpt-ML.
    Language: English
    Type: conferenceobject , doc-type:conferenceObject
    Library Location Call Number Volume/Issue/Year Availability
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    OPUS
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