Actual software development processes define the different steps developers have to perform during a development project. Usually these development steps are not described independently from each other—a more or less...Actual software development processes define the different steps developers have to perform during a development project. Usually these development steps are not described independently from each other—a more or less formal flow of development step is an essential part of the development process definition. In practice, we observe that often the process definitions are hardly used and very seldom “lived”. One reason is that the predefined general process flow does not reflect the specific constraints of the individual project. For that reasons we claim to get rid of the process flow definition as part of the development process. Instead we describe in this paper an approach to smartly assist developers in software process execution. The approach observes the developer’s actions and predicts his next development step based on the project process history. Therefore we apply machine learning resp. sequence learning approaches based on a general rule based process model and its semantics. Finally we show two evaluations of the presented approach: The data of the first is derived from a synthetic scenario. The second evaluation is based on real project data of an industrial enterprise.展开更多
Hydrogen,recognized as a critical energy source,requires green production methods,such as proton exchange membrane water electrolysis(PEMWE)powered by renewable energy.This is a key step toward sustainable development...Hydrogen,recognized as a critical energy source,requires green production methods,such as proton exchange membrane water electrolysis(PEMWE)powered by renewable energy.This is a key step toward sustainable development,with economic analysis playing an essential role.Life cycle costing(LCC)is commonly used to evaluate economic feasibility,but traditional LCC analyses often provide a single cost outcome,which limits their applicability across diverse regional contexts.To address these challenges,a Python-based tool is developed in this paper,integrating a bottom-up approach with net present value(NPV)calculations and Monte Carlo simulations.The tool allows users to manage uncertainty by intervening in the input data,producing a range of outcomes rather than a single deterministic result,thus offering greater flexibility in decision-making.Applying the tool to a 5 MW PEMWE plant in Germany,the total cost of ownership(TCO)is estimated to range between€52 million and€82.5 million,with hydrogen production costs between 5.5 and 11.4€/kg H2.There is a 95%probability that actual costs fall within this range.Sensitivity analysis reveals that energy prices are the key contributors to LCC,accounting for 95%of the variance in LCC,while iridium,membrane materials,and power electronics contribute to 75%of the variation in construction-phase costs.These findings underscore the importance of renewable energy integration and circular economy strategies in reducing LCC.展开更多
Introduction Plant phenotyping describes the result of the interaction of genotype with the environment[1].This is performed with high throughput in greenhouses by automated screening systems using different types of ...Introduction Plant phenotyping describes the result of the interaction of genotype with the environment[1].This is performed with high throughput in greenhouses by automated screening systems using different types of imaging and non-imaging sensors[2].The high-throughput imaging routines result in large amounts of data,which require sophisticated processing routines.Sharing and reusing phenotype-related data are not common,because its acquisition and processing are resource costly and technically intensive[3].展开更多
文摘Actual software development processes define the different steps developers have to perform during a development project. Usually these development steps are not described independently from each other—a more or less formal flow of development step is an essential part of the development process definition. In practice, we observe that often the process definitions are hardly used and very seldom “lived”. One reason is that the predefined general process flow does not reflect the specific constraints of the individual project. For that reasons we claim to get rid of the process flow definition as part of the development process. Instead we describe in this paper an approach to smartly assist developers in software process execution. The approach observes the developer’s actions and predicts his next development step based on the project process history. Therefore we apply machine learning resp. sequence learning approaches based on a general rule based process model and its semantics. Finally we show two evaluations of the presented approach: The data of the first is derived from a synthetic scenario. The second evaluation is based on real project data of an industrial enterprise.
基金funded by the Energie-Forschungszentrum Niedersachsen(efzn)in Germany,the SeLeKT-H2 project,and the Open Access Publishing Fund of Clausthal University of Technology.
文摘Hydrogen,recognized as a critical energy source,requires green production methods,such as proton exchange membrane water electrolysis(PEMWE)powered by renewable energy.This is a key step toward sustainable development,with economic analysis playing an essential role.Life cycle costing(LCC)is commonly used to evaluate economic feasibility,but traditional LCC analyses often provide a single cost outcome,which limits their applicability across diverse regional contexts.To address these challenges,a Python-based tool is developed in this paper,integrating a bottom-up approach with net present value(NPV)calculations and Monte Carlo simulations.The tool allows users to manage uncertainty by intervening in the input data,producing a range of outcomes rather than a single deterministic result,thus offering greater flexibility in decision-making.Applying the tool to a 5 MW PEMWE plant in Germany,the total cost of ownership(TCO)is estimated to range between€52 million and€82.5 million,with hydrogen production costs between 5.5 and 11.4€/kg H2.There is a 95%probability that actual costs fall within this range.Sensitivity analysis reveals that energy prices are the key contributors to LCC,accounting for 95%of the variance in LCC,while iridium,membrane materials,and power electronics contribute to 75%of the variation in construction-phase costs.These findings underscore the importance of renewable energy integration and circular economy strategies in reducing LCC.
基金partially funded by the Deutsche Forschungsgemeinschaft(DFGGerman Research Foundation)under Germany’s Excellence Strategy—EXC 2070-390732324support by the Open Access Publishing Fund of Clausthal University of Technology.
文摘Introduction Plant phenotyping describes the result of the interaction of genotype with the environment[1].This is performed with high throughput in greenhouses by automated screening systems using different types of imaging and non-imaging sensors[2].The high-throughput imaging routines result in large amounts of data,which require sophisticated processing routines.Sharing and reusing phenotype-related data are not common,because its acquisition and processing are resource costly and technically intensive[3].
