Erosion-corrosion of a stainless steel distillation column in food industry

Unusual extended corrosion phenomena were detected in a distillation column made in AISI type 316 stainless steel (UNS S31600) of a plant for natural pectins extraction from citrus. The column was the first of a series of two distillation columns representing the unit core. Corrosion problems were observed only in that column and mainly along the surface of the trays located in the lower section of the column. The phenomenon was observed subsequently to a modification of the original plant layout that caused an increase of the operating temperatures and turbulence of the process stream inside the column.     

New conductive thermoplastic elastomers. Part II. Physical and chemical‐physical characterization

To overcome the problems relevant to the low processbility and scarce mechanical properties of polyaniline, in a previous work we have proposed the synthesis of elastomeric conducting copolymers prepared by grafting polyaniline (EB) or sulfonated polyaniline (SPAN) chains to the backbone of a carboxylated segmented polyurethane (PEUA). In the present work the physical and chemical‐physical properties of the copolymers are investigated. As evidenced by thermal (DSC) and dynamo‐mechanical (DMTA) characterization, the introduction of EB or SPAN in the matrix enhances the hard‐soft phase segregation effect, because of the strong tendency of the conductive polymer chains to aggregate. Moreover, the EB and SPAN chains, grafted to the polyurethane backbone, acting as reinforcing filler, give rise, compared with the mechanical properties of the insulating matrix, to an increase of the Young modulus and a decrease of the tensile set. When the copolymers are HCl doped their electrical conductivity increases many orders of magnitude, reaching values of about 10^?3 Ω^?1 cm^?1. The conductivity, measured along the deformation direction, may be further increased by stretching the copolymer films. © 2002 Wiley Periodicals, Inc. J Appl Polym Sci 86: 1259–1264, 2002     

Bioactivity of Phenanthrenes from Juncus acutus on Selenastrum capricornutum

Twenty-five 9,10-dihydrophenanthrenes, four phenanthrenes, a dihydrodibenzoxepin, and a pyrene, isolated from the wetland plant Juncus acutus, were tested to detect their effects on the green alga Selenastrum capricornutum. Nine of the compounds were isolated and identified for the first time. Most of the compounds caused inhibition of algal growth. The 9,10-dihydrophenanthrenes 1, 5, 21, and 22 were the most active.     

A new contact model for the discrete element method simulation of $$\hbox {TiO}_2$$ nanoparticle films under mechanical load

We develop a novel coarse-grained contact model for Discrete Element Method simulations of \(\hbox {TiO}_2\) nanoparticle films subjected to mechanical stress. All model elements and parameters are derived in a self-consistent and physically sound way from all-atom Molecular Dynamics simulations of interacting particles and surfaces. In particular, the nature of atomic-scale friction and dissipation effects is taken into account by explicit modelling of the surface features and water adsorbate layers that strongly mediate the particle-particle interactions. The quantitative accuracy of the coarse-grained model is validated against all-atom simulations of \(\hbox {TiO}_2\) nanoparticle agglomerates under tensile stress. Moreover, its predictive power is demonstrated with calculations of force-displacement curves of entire nanoparticle films probed with force spectroscopy. The simulation results are compared with Atomic Force Microscopy and Transmission Electron Microscopy experiments.     

Evaluating the performance of machine learning methods and variable selection methods for predicting difficult-to-measure traits in Holstein dairy cattle using milk infrared spectral data

Fourier-transform infrared (FTIR) spectroscopy is a powerful high-throughput phenotyping tool for predicting traits that are expensive and difficult to measure in dairy cattle. Calibration equations are often developed using standard methods, such as partial least squares (PLS) regression. Methods that employ penalization, rank-reduction, and variable selection, as well as being able to model the nonlinear relations between phenotype and FTIR, might offer improvements in predictive ability and model robustness. This study aimed to compare the predictive ability of 2 machine learning methods, namely random forest (RF) and gradient boosting machine (GBM), and penalized regression against PLS regression for predicting 3 phenotypes differing in terms of biological meaning and relationships with milk composition (i.e., phenotypes measurable directly and not directly in milk, reflecting different biological processes which can be captured using milk spectra) in Holstein-Friesian cattle under 2 cross-validation scenarios. The data set comprised phenotypic information from 471 Holstein-Friesian cows, and 3 target phenotypes were evaluated: (1) body condition score (BCS), (2) blood β-hydroxybutyrate (BHB, mmol/L), and (3) κ-casein expressed as a percentage of nitrogen (κ-CN, % N). The data set was split considering 2 cross-validation scenarios: samples-out random in which the population was randomly split into 10-folds (8-folds for training and 1-fold for validation and testing); and herd/date-out in which the population was randomly assigned to training (70% herd), validation (10%), and testing (20% herd) based on the herd and date in which the samples were collected. The random grid search was performed using the training subset for the hyperparameter optimization and the validation set was used for the generalization of prediction error. The trained model was then used to assess the final prediction in the testing subset. The grid search for penalized regression evidenced that the elastic net (EN) was the best regularization with increase in predictive ability of 5%. The performance of PLS (standard model) was compared against 2 machine learning techniques and penalized regression using 2 cross-validation scenarios. Machine learning methods showed a greater predictive ability for BCS (0.63 for GBM and 0.61 for RF), BHB (0.80 for GBM and 0.79 for RF), and κ-CN (0.81 for GBM and 0.80 for RF) in samples-out cross-validation. Considering a herd/date-out cross-validation these values were 0.58 (GBM and RF) for BCS, 0.73 (GBM and RF) for BHB, and 0.77 (GBM and RF) for κ-CN. The GBM model tended to outperform other methods in predictive ability around 4%, 1%, and 7% for EN, RF, and PLS, respectively. The prediction accuracies of the GBM and RF models were similar, and differed statistically from the PLS model in samples-out random cross-validation. Although, machine learning techniques outperformed PLS in herd/date-out cross-validation, no significant differences were observed in terms of predictive ability due to the large standard deviation observed for predictions. Overall, GBM achieved the highest accuracy of FTIR-based prediction of the different phenotypic traits across the cross-validation scenarios. These results indicate that GBM is a promising method for obtaining more accurate FTIR-based predictions for different phenotypes in dairy cattle.     

A theoretical model of the relationship between the <Emphasis Type="Italic">h</Emphasis>-index and other simple citation indicators

In this study, we quantitatively compared the impact of mission-oriented research grants and curiosity-driven grants on the diversity of research subjects in Japan. First, we examined data for Japanese principal investigators receiving research funding between 2000 and 2010 in the field of nanotechnology and materials science, and identified groups of researchers whose publication performance was positively affected by the mission-oriented grant, CREST. We then compared the effect of CREST with that of the curiosity-driven grant, KAKENHI. The analysis uses both propensity score matching and difference in differences (PSM-DID) methodologies. Our results show that for participants in the CREST program there was an increase in number of publications of more than 10% per year, for periods of both 5 and 3 years after the funding ended, even though the observed average effect on citation was not statistically significant. Second, we evaluated the diversity of research subjects through analysis of the distribution of the classification codes applied to articles published between 1996 and 2013, utilizing the J-Global database, which has the finest granularity of category among existing bibliographic scientific publication databases. Research subjects were better conserved under the mission-oriented program than the curiosity-driven one, a finding contrary to predictions of conventional theory. We also found that under mission-oriented funding, there was an increase in diversity in the sense of marginal utility. These findings should be of use in the “diversity-aware” design of programs for the funding of fundamental research.