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For production of sheet metal parts for car bodies, an adjustment of process parameters is required to maintain the desired part quality in presence of scattering blank properties. The digital transformation enables the application of data-driven methods for finding process parameters instead of a time-consuming experience-driven trial-and-error approach. However, due to cost and technical limitations, it is still hard to measure quality for every part. Removing data points of low-quality parts helps recommending proper process parameters. In this paper, we propose classification-based solution for recommending process parameters. In data preprocessing, the solution utilizes anomaly detection and knowledge-based methods to remove potential data points of low-quality parts without quality measures. On the processed data, a classification model is trained to predict process parameters according to blank properties. Our solution detects 30% low-quality parts and gives competitive performance (92.26% prediction accuracy) compared to a model trained on data comprising quality measures.

Summary In water resources modeling, meteorological data scarcity can be compensated by various global data sets, but those data sets can differ tremendously. In the literature, hydrological models of differing complexity are proposed for estimating the water resources of semi-arid catchments, and also to evaluate rainfall data sets. The goal of this paper is to provide a joint analysis of modeling uncertainty due to different input data and increasing model complexity. Impacts of mutually concealed uncertainties on model performance and model outputs are exemplified in two data sparse semi-arid catchments in Ethiopia. We applied a semi-distributed and a fully distributed hydrological model, having different levels of complexity. Three different satellite-based rainfall data sets and two temperature products were used as model inputs. The semi-distributed model demonstrated good validation performances, while the fully distributed model was more sensitive to data uncertainties. The application of TRMM version 6 completely failed and the high-resolution CMORPH precipitation estimate outperformed TRMM version 7. In contrast, the use of high-resolution temperature data did not improve the model results. The large differences in remotely sensed input data were buffered inside the hydrological models. Therefore, data set evaluations regarding only the simulated hydrographs were less meaningful. In contrast, the investigation of parameter evolution and distributed outputs’ variability appeared to be a valuable tool to uncover the interdependencies of data and model uncertainties. We suggest this procedure to be applied by default in water resources estimations that are affected by data scarcity, but especially when data sets are evaluated using hydrological models. Our case study demonstrates that estimations of groundwater recharge and actual evapotranspiration vary largely, depending on the applied data sets and models. The joint analysis reveals large interdependencies between data and model evaluations. It shows that traditional studies focusing only on one part of uncertainty, either the input uncertainty or the uncertainty arising from the choice of model structure are limited in their explanatory power of the modeling performance, particularly in poorly gauged regions.

Summary A key factor for sustainable management of groundwater systems is the accurate estimation of groundwater recharge. Hydrological models are common tools for such estimations and widely used. As such models need to be calibrated against measured values, the absence of adequate data can be problematic. We present a nested multi-response calibration approach for a semi-distributed hydrological model in the semi-arid catchment of Wadi al Arab in Jordan, with sparsely available runoff data. The basic idea of the calibration approach is to use diverse observations in a nested strategy, in which sub-parts of the model are calibrated to various observation data types in a consecutive manner. First, the available different data sources have to be screened for information content of processes, e.g. if data sources contain information on mean values, spatial or temporal variability etc. for the entire catchment or only sub-catchments. In a second step, the information content has to be mapped to relevant model components, which represent these processes. Then the data source is used to calibrate the respective subset of model parameters, while the remaining model parameters remain unchanged. This mapping is repeated for other available data sources. In that study the gauged spring discharge (GSD) method, flash flood observations and data from the chloride mass balance (CMB) are used to derive plausible parameter ranges for the conceptual hydrological model J2000g. The water table fluctuation (WTF) method is used to validate the model. Results from modelling using a priori parameter values from literature as a benchmark are compared. The estimated recharge rates of the calibrated model deviate less than ±10% from the estimates derived from WTF method. Larger differences are visible in the years with high uncertainties in rainfall input data. The performance of the calibrated model during validation produces better results than applying the model with only a priori parameter values. The model with a priori parameter values from literature tends to overestimate recharge rates with up to 30%, particular in the wet winter of 1991/1992. An overestimation of groundwater recharge and hence available water resources clearly endangers reliable water resource managing in water scarce region. The proposed nested multi-response approach may help to better predict water resources despite data scarcity.

Environmental modeling frameworks support scientific model development by providing model developers with domain specific software libraries which are used to aid model implementation. This paper presents an investigation on the framework invasiveness of environmental modeling frameworks. Invasiveness, similar to object-oriented coupling, is defined as the quantity of dependencies between model code and a modeling framework. We investigated relationships between invasiveness and the quality of modeling code, and also the utility of using a lightweight framework design approach in an environmental modeling framework. Five metrics to measure framework invasiveness were proposed and applied to measure dependencies between model and framework code of several implementations of Thornthwaite and the Precipitation-Runoff Modeling System (PRMS), two well-known hydrological models. Framework invasiveness measures were compared with existing common software metrics including size (lines of code), cyclomatic complexity, and object-oriented coupling. Models with lower framework invasiveness tended to be smaller, less complex, and have less coupling. In addition, the lightweight framework implementations of the Thornthwaite and PRMS models were less invasive than the traditional framework model implementations. Our results show that model implementations with higher degrees of framework invasiveness also had structural characteristics which previously have been shown to predict poor maintainability, a non-functional code quality attribute of concern. We conclude that using a framework with a lightweight framework design shows promise in helping to improve the quality of model code and that the lightweight framework design approach merits further attention by environmental modeling framework developers.

The sustainability of the rice–wheat cropping system in an irrigated semi-arid area of Haryana State (India) is under threat due to the continuous rise in the poor quality groundwater table, which is caused by the geo-hydrological condition and poor irrigation water management. About 500,000 ha in the State are waterlogged and unproductive and the size of the waterlogged area is increasing. We analyse the hydrology and estimate seasonal net groundwater recharge in the study area. Rainfall is quite variable, particularly in the monsoon season, and the mean monthly reference evapotranspiration shows a high inter-annual variation, with values between 2.45 and 8.47 mm/day in December and May. Groundwater recharge analysis during the study period (1989–2008) reveals that percolation from irrigated fields is the main recharge component with 57% contribution to the total recharge. An annual groundwater table rise of 0.137 m has been estimated for the study area. As the water table has been rising continuously, suitable water management strategies such as increasing groundwater abstraction by installing more tubewells, using the groundwater conjunctively with good quality canal water, changes in cropping patterns, adoption of salt tolerant crops, changes in water-pricing policy, and matching water supply more closely with demand, are suggested to bring the water table down to a safe limit and to prevent further rising of the water table.

In this paper the J2000, a new modelling system for the distributed simulation of the water balance in large river basins, will be introduced and described briefly. The introduction comprises an overview of the model's purpose and concept, the simulated hydrological processes, the distribution concept for the spatial subdivision of the catchment as well as the methods for regionalisation of climate data from point sources. As an example for the model's capabilities simulation results of a large catchment in Germany are shown. The model description is complemented by the presentation of results of two land use change scenarios to prove the model's ability to be used for the spatial quantification of the impact of land use changes on the water balance.

In recent years, many methods have been developed for the different datamining fields. One of the fields is fuzzy modeling, which is predominantly applied, if interpretable results are desired. In order to build and test fuzzy models, powerful software tools for fuzzy modeling and simulation are needed. In this paper two programs, WINROSA and DORA for Windows, are presented, which fullfill both requirements. WINROSA incorporates the Fuzzy-ROSA method, which is a method for automatic rule generation on the basis of relevance tests. DORA for Windows is a simulation tool with emphasis on fuzzy systems. The features of both programs are presented and it is shown how both programs complement each other.

The ultrastructure of dense-packed o/w emulsions prepared from unmodified and acetylated protein isolates as well as from purified legumin from faba beans was studied using transmission electron microscopy. After a centrifugal test in a preparative ultracentrifuge, a transparent layer was observed that could be separated from the emulsion and prepared for electron microscopy. The transparent layer was characterized by densely packed, mostly deformed oil droplets stabilized by interfacial protein films with a thickness between 20 and 30 nm.

The aim of this work was to investigate whether product inhibiton of sucrose-phosphate synthase (SPS) by sucrose-6-phosphate (sucrose6P) plays a significant role in feedback regulation of sucrose synthesis in spinach leaves. The amount of sucrose6P in leaves was very low (0.6-0.7 nmol mg −1 chlorophyll) and did not increase during the day when sucrose accumulated in the leaf. Sucrose6P did not have any significant inhibitory effect on partially purified spinach leaf SPS, and investigation of the reverse reaction revealed a high apparent K m of 6 mM for sucrose6P. These results, together with previous measurements of cytosolic metabolite levels in spinach leaves, were used to calculate improved values for the in vivo free energy changes of the reactions catalysed by SPS and sucrose-6-phosphatase. Both reactions are significantly displaced from equilibrium. It is concluded that direct product inhibition by sucrose6P does not play an important role in the regulation of spinach leaf SPS.

Experiments were carried out to assess whether pyrophosphate: fructose-6-phosphate phosphotransferase (PFP) could contribute to the turnover of inorganic pyrophosphate during photosynthetic sucrose synthesis. It was found that: (i) the maximum catalytic activity of PFP exceeded the rate of sucrose synthesis in spinach and barley leaves. Comparison of the measured fluxes and enzyme activities with the previously measured concentrations of pyrophosphate, fructose-6-phosphate and fructose-1,6-bisphosphate suggested that PFP could catalyse the removal of the pyrophosphate produced during sucrose synthesis, provided the cytosolic phosphate concentration were to fall below about 4 mM. (ii) The pyrophosphate level decreased slightly during the induction of photosynthesis in saturating light and CO2. (iii) Pyrophosphate remained high after supplying mannose to leaves to deplete the cytosolic phosphate, and increased after supplying phosphate to leaves. It is discussed how these results provide evidence for a tight regulation of the pyrophosphate concentration, and are consistent with PFP playing a role.