The Aerobic Biodegradation Kinetics of Plant Tannins in Industrial Wastewater

This paper describes an experimental determination of the biodegradation rate for tannins present in industrial wastewater, after the extraction of chestnut chips. Experiments were performed in a laboratory aerobic reactor (Armfield) by using biomass from an existing industrial wastewater treatment plant. The outlet tannins concentration was determined under various processing conditions. Simultaneously, an optical microscope was used to monitor the mix of microbiological cultures in the biomass. On the basis of data obtained in experiments, non-linear regression was used to perform parametric analysis of various kinetic models, which took into account inhibition, as quoted in literature (Haldane, Edwards, Aiba, Luong). The statistical analysis, based on the P-criterion, F-criterion, adjusted coefficient of determination, Kolmogorov-Smirnov test and root mean squared error, showed that the biodegradation of plant tannins in industrial wastewater under selected conditions for aerobic digestion, can be most successfully described statistically by the Aiba\u27s kinetic model

The Aerobic Biodegradation Kinetics of Plant Tannins in Industrial Wastewater

This paper describes an experimental determination of the biodegradation rate for tannins present in industrial wastewater, after the extraction of chestnut chips. Experiments were performed in a laboratory aerobic reactor (Armfield) by using biomass from an existing industrial wastewater treatment plant. The outlet tannins concentration was determined under various processing conditions. Simultaneously, an optical microscope was used to monitor the mix of microbiological cultures in the biomass. On the basis of data obtained in experiments, non-linear regression was used to perform parametric analysis of various kinetic models, which took into account inhibition, as quoted in literature (Haldane, Edwards, Aiba, Luong). The statistical analysis, based on the P-criterion, F-criterion, adjusted coefficient of determination, Kolmogorov-Smirnov test and root mean squared error, showed that the biodegradation of plant tannins in industrial wastewater under selected conditions for aerobic digestion, can be most successfully described statistically by the Aiba\u27s kinetic model

Hypothetical Reasoning via Provenance Abstraction

Data analytics often involves hypothetical reasoning: repeatedly modifying the data and observing the induced effect on the computation result of a data-centric application. Previous work has shown that fine-grained data provenance can help make such an analysis more efficient: instead of a costly re-execution of the underlying application, hypothetical scenarios are applied to a pre-computed provenance expression. However, storing provenance for complex queries and large-scale data leads to a significant overhead, which is often a barrier to the incorporation of provenance-based solutions. To this end, we present a framework that allows to reduce provenance size. Our approach is based on reducing the provenance granularity using user defined abstraction trees over the provenance variables; the granularity is based on the anticipated hypothetical scenarios. We formalize the tradeoff between provenance size and supported granularity of the hypothetical reasoning, and study the complexity of the resulting optimization problem, provide efficient algorithms for tractable cases and heuristics for others. We experimentally study the performance of our solution for various queries and abstraction trees. Our study shows that the algorithms generally lead to substantial speedup of hypothetical reasoning, with a reasonable loss of accuracy

Big Data Analysis

The value of big data is predicated on the ability to detect trends and patterns and more generally to make sense of the large volumes of data that is often comprised of a heterogeneous mix of format, structure, and semantics. Big data analysis is the component of the big data value chain that focuses on transforming raw acquired data into a coherent usable resource suitable for analysis. Using a range of interviews with key stakeholders in small and large companies and academia, this chapter outlines key insights, state of the art, emerging trends, future requirements, and sectorial case studies for data analysis

Reflective, polarizing, and magnetically soft amorphous Fe/Si multilayer neutron optics with isotope-enriched 11B4C inducing atomically flat interfaces

The utilization of polarized neutrons is of great importance in scientific disciplines spanning materials science, physics, biology, and chemistry. Polarization analysis offers insights into otherwise unattainable sample information such as magnetic domains and structures, protein crystallography, composition, orientation, ion-diffusion mechanisms, and relative location of molecules in multicomponent biological systems. State-of-the-art multilayer polarizing neutron optics have limitations, particularly low specular reflectivity and polarization at higher scattering vectors/angles, and the requirement of high external magnetic fields to saturate the polarizer magnetization. Here, we show that by incorporating 11B4C into Fe/Si multilayers, amorphization and smooth interfaces can be achieved, yielding higher neutron reflectivity, less diffuse scattering and higher polarization. Magnetic coercivity is eliminated, and magnetic saturation can be reached at low external fields (>2 mT). This approach offers prospects for significant improvement in polarizing neutron optics, enabling; nonintrusive positioning of the polarizer, enhanced flux, increased data accuracy, and further polarizing/analyzing methods at neutron scattering facilities