Validation of scientific topic models using graph analysis and corpus metadata

Probabilistic topic modeling algorithms like Latent Dirichlet Allocation (LDA) have become powerful tools for the analysis of large collections of documents (such as papers, projects, or funding applications) in science, technology an innovation (STI) policy design and monitoring. However, selecting an appropriate and stable topic model for a specific application (by adjusting the hyperparameters of the algorithm) is not a trivial problem. Common validation metrics like coherence or perplexity, which are focused on the quality of topics, are not a good fit in applications where the quality of the document similarity relations inferred from the topic model is especially relevant. Relying on graph analysis techniques, the aim of our work is to state a new methodology for the selection of hyperparameters which is specifically oriented to optimize the similarity metrics emanating from the topic model. In order to do this, we propose two graph metrics: the first measures the variability of the similarity graphs that result from different runs of the algorithm for a fixed value of the hyperparameters, while the second metric measures the alignment between the graph derived from the LDA model and another obtained using metadata available for the corresponding corpus. Through experiments on various corpora related to STI, it is shown that the proposed metrics provide relevant indicators to select the number of topics and build persistent topic models that are consistent with the metadata. Their use, which can be extended to other topic models beyond LDA, could facilitate the systematic adoption of this kind of techniques in STI policy analysis and design.

     

Technological and sensorial properties of liquid nitrogen ice cream enriched with protein from brewing waste (trub)

Debittered trub (brewing waste) is an important source of protein source (70.26%). Trub and whey protein were used for 5% protein enrichment of ice cream frozen by liquid nitrogen. Three formulations were elaborated: ice cream standard (ICS), ice cream with whey protein (ICW) and ice cream with trub (ICT). Chemical composition, rheological properties, texture, overrun, melting rate, scanning electron microscopy and a sensorial test were performed. Results showed that ICT presented a higher viscosity, obtained on the upward curve up to 6.76 Pa s⁻¹, consistency index (22.96 (Pa s⁻¹)ⁿ), hysteresis area (140.40 mPa s⁻¹) and hardness (31113.33 g) but a lower melting rate (0.38 g min⁻¹), overrun (13.92%) and sensorial acceptability than the other formulations. The addition of trub debittered for protein enrichment improved ice cream properties and demonstrated that it could be used as a food ingredient.     

Kinetic,thermodynamic parameters and in vitro digestion of tannase from Aspergillus tamarii URM 7115

Tannase is an enzyme used in various industries and produced by a large number of microorganisms. The aim of this study was to evaluate tannase production to determine the biochemical, kinetic, and thermodynamic properties and to simulate tannase in vitro digestion. The tannase-producing fungal strain was isolated from “jamun” leaves and identified as Aspergillus tamarii. Temperature at 26°C for 67?h was the best combination for maximum tannase activity (6.35-fold; initial activity in Plackett–Burman design—15.53?U/mL and average final activity in Doehlert design—98.68?U/mL). The crude extract of tannase was optimally active at 40°C, pH 5.5 and 6.5. Moreover, tannase was stimulated by Na⁺, Ca²⁺, Mg²⁺, and Mn²⁺. The half-life at 40°C lasted 247.55?min. The free energy of Gibbs, enthalpy, and entropy, at 40°C, was 81.47, 16.85, and ?0.21?kJ/mol?·?K, respectively. After total digestion, 123.95% of the original activity was retained. Results suggested that tannase from A. tamarii URM 7115 is an enzyme of interest for industrial applications, such as gallic acid production, additive for feed industry, and for beverage manufacturing, due to its catalytic and thermodynamic properties.     

Development of New Sulphonyl Resin from Modification of Commercial Resin

Summary This article deals with the handy synthesis of sulfonyl resins, which were produced by the treatment of a commercial sulfonic resin (Lewatit VPOC1812^® based at divinylbenzene (DVB) and styrene (STY). The preliminary chemical modification was based on the reaction of the Lewatit VPOC1812^® with thionyl chloride aiming to produce the sulfonyl chloride groups. The best conditions to obtain the sulfonyl chloride groups were: SOCl₂/SO₃H (molar ratio) =13 at 79 °C during 72 h. The resin chlorinated was afterward treated with urea, thiourea or guanidine. The functionalized resins with urea, thiourea or guanidine were produced with 56, 68 and 93% yield, respectively. The commercial and modified resins were characterized by apparent density, swelling degree, elemental analysis (CHNS), FTIR, optical microscopy (OM) and scanning electron microscopy (SEM).     

Hybrid Approach for Addressing Uncertainty in Risk Assessments

Parameter uncertainty is a major aspect of the model-based estimation of the risk of human exposure to pollutants. The Monte Carlo method, which applies probability theory to address model parameter uncertainty, relies on a statistical representation of available information. In recent years, other uncertainty theories have been proposed as alternative approaches to address model parameter uncertainty in situations where available information is insufficient to identify statistically representative probability distributions, due in particular to data scarcity. The simplest such theory is possibility theory, which uses so-called fuzzy numbers to represent model parameter uncertainty. In practice, it may occur that certain model parameters can be reasonably represented by probability distributions, because there are sufficient data available to substantiate such distributions by statistical analysis, while others are better represented by fuzzy numbers (due to data scarcity). The question then arises as to how these two modes of representation of model parameter uncertainty can be combined for the purpose of estimating the risk of exposure. This paper proposes an approach (termed a hybrid approach) which combines Monte Carlo random sampling of probability distribution functions with fuzzy calculus. The approach is applied to a real case of estimation of human exposure, via vegetable consumption, to cadmium present in the surficial soils of an industrial site located in the north of France. The application illustrates the potential of the proposed approach, which allows the uncertainty affecting model parameters to be represented in a way that is consistent with the information at hand. Also, because the hybrid approach takes advantage of the “rich” information provided by probability distributions, while retaining the conservative character of fuzzy calculus, it is believed to hold value in terms of a “reasonable” application of the precautionary principle.     

Aide in decision-making: contribution to uncertainties in three-dimensional measurement

The authorities of the standards organization International Organization of Standardization (ISO) advocate mastering any uncertainties in all parts of the industrialization process. In the three-dimensional (3D) measurement process, uncertainty is usually obtained at the end of a battery of tests. It is defined as a whole because it includes several types of errors, known systematic components, unknown systematic components and random components. Automated calculations of uncertainty can be made based on statistics. This method is based on statistical concepts, which are in accordance with “The Guide to the expression of the uncertainty in measurement” (GUM). It also enables us to generate uncertainties on the verification of ISO specifications (or specs in the ISO directives). In the course of this article, a usage will be presented that takes the knowledge of uncertainties into account: this usage will help the operator to take a decision on the conformance of a mechanical part in reference to its conformance to geometric tolerance.     

The Effect of Ozone Gas-Liquid Contacting Conditions in a Static Mixer on Microorganism Reduction

The effect of gas-liquid contacting conditions in a static mixer on ozone transfer efficiency and reduction of Bacillus subtilis spores was studied in an experimental ozone contactor. An empirical mathematical model was developed that related the transfer efficiency in the experimental system to the superficial liquid velocity in the mixer, the gas-liquid flow rate ratio and the height of the down-stream bubble column. Spore reduction was determined primarily by the dissolved ozone concentration-time (C_avgt_m) product in the reactive flow segment and was independent of the gas-liquid contacting conditions in the static mixer. In an integrated ozone contacting system, the static mixer should be designed to maximize ozone mass transfer while the reactive flow segment should be designed for efficient microorganism reduction.