Use of Response Surface Methodology to Investigate the Effectiveness of Commercial Enzymes on Buckwheat Malt for Brewing Purposes

The influence of two factors, total concentration and fraction of three pairs of commercial enzymes, which showed statistical significance (Biocellulase W with Hitempase 2XL, Biocellulase W with Amylo 300 and Amylo 300 with Hitempase 2XL), were studied for their overall effect on buckwheat wort quality using response surface methodology (RSM). This study revealed that the addition of increasing levels of Hitempase 2XL to the buckwheat mash increased colour, extract levels, wort filtration, fermentability and total fermentable extract (TFE), along with decreasing viscosity values. Results also determined a high level of fermentability when an enzyme combination of 30% Biocellulase and 70% Hitempase was added to the mash. The addition of increasing levels of Amylo 300 to buckwheat mashes resulted in increases in fermentability and total fermentable extract (TFE), along with increases in total soluble nitrogen (TSN), free amino nitrogen (FAN) and Kolbach index (KI). With regard to the proposed optimal regime, although no synergistic effect was found when the three enzymes were used together, the optimum conditions for the production of buckwheat wort with lowest viscosity, highest extract and optimal fermentability were achieved using a joint model. Overall, the findings of this study demonstrate the feasibility of producing wort suitable for the brewing of gluten‐free beer from 100% malted buckwheat with careful optimisation of enzyme types and dosage levels.     

LoVis: Local Pattern Visualization for Model Refinement

Linear models are commonly used to identify trends in data. While it is an easy task to build linear models using pre‐selected variables, it is challenging to select the best variables from a large number of alternatives. Most metrics for selecting variables are global in nature, and thus not useful for identifying local patterns. In this work, we present an integrated framework with visual representations that allows the user to incrementally build and verify models in three model spaces that support local pattern discovery and summarization: model complementarity, model diversity, and model representivity. Visual representations are designed and implemented for each of the model spaces. Our visualizations enable the discovery of complementary variables, i.e., those that perform well in modeling different subsets of data points. They also support the isolation of local models based on a diversity measure. Furthermore, the system integrates a hierarchical representation to identify the outlier local trends and the local trends that share similar directions in the model space. A case study on financial risk analysis is discussed, followed by a user study.     

The application of inductively coupled plasma mass spectrometry in clinical pharmacological oncology research

Metal-based anticancer agents are frequently used in the treatment of a wide variety of cancer types. The monitoring of these anticancer agents in biological samples is important to understand their pharmacokinetics, pharmacodynamics, and metabolism. In addition, determination of metals originating from anticancer agents is relevant to assess occupational exposure of health care personnel working with these drugs. The high sensitivity of inductively coupled plasma mass spectrometry (ICP-MS) has resulted in an increased popularity of this technique for the analysis of metal-based anticancer drugs. In addition to the quantitative analysis of the metal of interest in a sample, ICP-MS can be used as an ultrasensitive metal selective detector in combination with speciation techniques such as liquid chromatography. In the current review we provide a systematic survey of publications describing the analysis of platinum- and ruthenium-containing anticancer agents using ICP-MS, focused on the determination of total metal concentrations and on the speciation of metal compounds in biological fluids, DNA- and protein-adducts, and environmental samples. We conclude that ICP-MS is a powerful tool for the quantitative analysis of metal-based anticancer agents from multiple sample sources.     

A non-autonomous optimal control model of renewable energy production under the aspect of fluctuating supply and learning by doing

Given the constantly raising world-wide energy demand and the accompanying increase in greenhouse gas emissions that pushes the progression of climate change, the possibly most important task in future is to find a carbon-low energy supply that finds the right balance between sustainability and energy security. For renewable energy generation, however, especially the second aspect turns out to be difficult as the supply of renewable sources underlies strong volatility. Further on, investment costs for new technologies are so high that competitiveness with conventional energy forms is hard to achieve. To address this issue, we analyze in this paper a non-autonomous optimal control model considering the optimal composition of a portfolio that consists of fossil and renewable energy and which is used to cover the energy demand of a small country. While fossil energy is assumed to be constantly available, the supply of the renewable resource fluctuates seasonally. We further on include learning effects for the renewable energy technology, which will underline the importance of considering the whole life span of such a technology for long-term energy planning decisions.     

Recommendation of RILEM TC 238-SCM: determination of the degree of reaction of siliceous fly ash and slag in hydrated cement paste by the selective dissolution method

The performance of slag and fly ash in hydrated cementitious materials depends on the degree of reaction developed at the evaluated age. Several methods for the determination of the reaction degree of supplementary cementitious materials are available, among which the selective dissolution method is one of methods developed the earliest. This is a direct method that aims to quantify the amount of unreacted slag or fly ash in the sample by applying a selective acid attack. The degree of reaction is obtained from the comparison between the remaining unreacted SCM, which should not dissolve, and the total amount initially included in the mix. This recommendation indicates suitable procedures for computing the degree of reaction by selective dissolution of cement pastes containing slag and fly ash. Specific considerations are indicated for necessary corrections due to the imperfect selective dissolution when the procedure is applied to hydrated cement paste.     

Symbolic Intersect Detection: A Method for Improving Spatial Intersect Joins

Due to the increasing popularity of spatial databases, researchers have focused their efforts on improving the query processing performance of the most expensive spatial database operation: the spatial join. While most previous work focused on optimizing the filter step, it has been discovered recently that, for typical GIS data sets, the refinement step of spatial join processing actually requires a longer processing time than the filter step. Furthermore, two-thirds of the time in processing the refinement step is devoted to the computation of polygon intersections. To address this issue, we therefore introduce a novel approach to spatial join optimization that drastically reduces the time of the refinement step. We propose a new approach called Symbolic Intersect Detection (SID) for early detection of true hits. Our SID optimization eliminates most of the expensive polygon intersect computations required by a spatial join by exploiting the symbolic topological relationships between the two candidate polygons and their overlapping minimum bounding rectangle. One important feature of our SID optimization is that it is complementary to the state-of-the-art methods in spatial join processing and therefore can be utilized by these techniques to further optimize their performance. In this paper, we also develop an analytical cost model that characterizes SIDs effectiveness under various conditions. Based on real map data, we furthermore conduct an experimental evaluation comparing the performance of the spatial joins with SID against the state-of-the-art approach. Our experimental results show that SID can effectively identify more than 80% of the true hits with negligible overhead. Consequently, with SID, the time needed for resolving polygon intersect in the refinement step is improved by over 50% over known techniques, as predicted by our analytical model.     

Skyline and mapping aware join query evaluation

Growing interests in multi-criteria decision support applications have resulted in a flurry of efficient skyline algorithms. In practice, real-world decision support applications require to access data from disparate sources. Existing techniques define the skyline operation to work on a single set, and therefore, treat skylines as an “add-on” on top of a traditional Select-Project-Join query plan. In many real-world applications, the skyline dimensions can be anti-correlated such as the attribute pair {price, mileage} for cars and {price, distance} for hotels. Anti-correlated data are particularly challenging for skyline evaluation and therefore have commonly been ignored by existing techniques. In this work, we propose a robust execution framework called SKIN to evaluate skyline over joins. The salient features of SKIN are: (a) effective in reducing the two primary costs, namely the cost of generating the join results and the cost of dominance comparisons to compute the final skyline of join results, (b) shown to be robust for both skyline-friendly (independent and correlated) as well as skyline-unfriendly (anti-correlated) data distributions. SKIN is effective in exploiting the skyline knowledge in both local within individual data sources and across disparate sources—to significantly reduce the above-mentioned costs incurred during the evaluation of skyline over join. Our experimental study demonstrates the superiority of our proposed approach over state-of-the-art techniques to handle a wide variety of data distributions.     

A generalized multiple layer data envelopment analysis model for hierarchical structure assessment: A case study in road safety performance evaluation

Data envelopment analysis (DEA) is a powerful analytical research tool for measuring the relative efficiency of a homogeneous set of decision making units (DMUs) by obtaining empirical estimates of relations between multiple inputs and multiple outputs related to the DMUs. To further embody multilayer hierarchical structures of these inputs and outputs in the DEA framework, which are prevalent in today’s performance evaluation activities, we propose a generalized multiple layer DEA (MLDEA) model. Starting from the input-oriented CCR model, we elaborate the mathematical deduction process of the MLDEA model, formulate the weights in each layer of the hierarchy, and indicate different types of possible weight restrictions. Meanwhile, its linear transformation is realized and further extended to the BCC form. To demonstrate the proposed MLDEA model, a case study in evaluating the road safety performance of a set of 19 European countries is carried out. By using 13 hierarchical safety performance indicators in terms of road user behavior (e.g., inappropriate or excessive speed) as the model’s input and 4 layered road safety final outcomes (e.g., road fatalities) as the output, we compute the most optimal road safety efficiency score for the set of European countries, and further analyze the weights assigned to each layer of the hierarchy. A comparison of the results with the ones from the one layer DEA model clearly indicates the usefulness and effectiveness of this improvement in dealing with a great number of performance evaluation activities with hierarchical structures.     

A concept and implementation of higher-level XML transformation languages

In the software development (e.g. with product lines or refactoring) transformations play an increasing role. To ease and automate these transformations, we propose a solution based on the operator hierarchy concept. It allows to define transformation operator hierarchies containing different levels of transformation operators. The operators capture reusable knowledge units. The concrete realization of such a higher-level transformation language construction is demonstrated by an application of the XML operator hierarchy concept to the transformation language XSLT. XSLT serves as an example which is employed to provide the elementary transformation operators. On top of these elementary operators the layered concept allows the definition of new higher-level operators, e.g. domain-independent and domain-specific ones. In an application example the construction of the higher-level language XML2DSV is presented. This is a stand-alone domain-specific transformation language, which can be used to create delimiter-separated values (DSV) files from XML documents, on the base of XSLT. We developed XTC (XML Transformation Coordinator) to automate the multi-level transformation process.