Analysis of trace elements in southern Italian wines and their classification according to provenance

Wines from three important wine-producing Southern Italian regions (Basilicata, Calabria and Campania) were characterized according to their content in macro-, micro-elements and lanthanides. The elemental composition was used in multivariate statistical analysis to discriminate the wines according to geographical origin. A total of 61 elements were determined in 120 red wines produced in the years 2000-2002, and precisely 40 samples for each region. Results indicated that element analysis provides a good prospect for discriminating wines by regions, even if the element composition is not dependent on the year of production of wine. A Canonical Variate Analysis showed that the discrimination of wines according to their provenance is based on the following elements: Ag, B, Ca, Cd, Eu, Fe, Ga, La, Lu, Mn, Nd, Pr, Sm, Th, Tm, V, Yb, and Zr for Basilicata; Al, B, Fe, Mg, Mn, Ti, Tl, Sc and Zn for Calabria; Ba, Eu, K, I, Rb, and Tl for Campania.     

Biometric,nutritional and sensory changes in intensively farmed Murray cod (Maccullochella peelii peelii, Mitchell) following different purging times

A period of purging before harvesting is common practice in intensive aquaculture to eliminate any possible off flavours from the fish. The present study was conducted to evaluate the biometrical, nutritional and sensory changes in intensively farmed Murray cod (Maccullochella peelii peelii) after 0, 2 and 4 weeks of purging. After the main biometric parameters were recorded, fish were analysed for proximate, fatty acid composition and flavour volatile compounds. A consumer preference test (triangle test) was also conducted to identify sensorial differences that may affect the consumer acceptability of the product.     

Deep learning-based surrogate modeling and optimization for microalgal biofuel production and photobioreactor design

Identifying optimal photobioreactor configurations and process operating conditions is critical to industrialize microalgae-derived biorenewables. Traditionally, this was addressed by testing numerous design scenarios from integrated physical models coupling computational fluid dynamics and kinetic modeling. However, this approach presents computational intractability and numerical instabilities when simulating large-scale systems, causing time-intensive computing efforts and infeasibility in mathematical optimization. Therefore, we propose an innovative data-driven surrogate modeling framework, which considerably reduces computing time from months to days by exploiting state-of-the-art deep learning technology. The framework built upon a few simulated results from the physical model to learn the sophisticated hydrodynamic and biochemical kinetic mechanisms; then adopts a hybrid stochastic optimization algorithm to explore untested processes and find optimal solutions. Through verification, this framework was demonstrated to have comparable accuracy to the physical model. Moreover, multi-objective optimization was incorporated to generate a Pareto-frontier for decision-making, advancing its applications in complex biosystems modeling and optimization. © 2018 American Institute of Chemical Engineers AIChE J, 65: 915–923, 2019     

A new use for glassy carbon: Development of LDPE/glassy carbon composites for antistatic packaging applications

Antistatic packaging is used for the protection and storage of electronic boards and sensitive electronic components. Different polyolefins can be used for the production of antistatic packaging; however, the accumulation of static electricity in these polyolefins may cause serious damage to the electronic components. In this study, since glassy carbon (GC) has good mechanical properties and electrical characteristics, it was used as an antistatic filler and blended with low-density polyethylene (LDPE). Composites of LDPE with GC in different proportions were prepared in the molten state using a homogenizer rotating at 3000 rpm. The composites were characterized in terms of thermal, mechanical, structural, and electrical properties. The GC is well dispersed and distributed in the LDPE, which contributed to the formation of a percolated network that enhanced antistatic characteristic. An increase of two orders of magnitudes in the electrical conductivity was obtained for the composite with 0.5 wt % of GC and thus it can be used as antistatic packaging. © 2018 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019 , 136, 47204.     

The role of collagen in bone apatite formation in the presence of hydroxyapatite nucleation inhibitors

Bone is a composite material in which collagen fibrils form a scaffold for a highly organized arrangement of uniaxially oriented apatite crystals. In the periodic 67 nm cross-striated pattern of the collagen fibril, the less dense 40-nm-long gap zone has been implicated as the place where apatite crystals nucleate from an amorphous phase, and subsequently grow. This process is believed to be directed by highly acidic non-collagenous proteins; however, the role of the collagen matrix during bone apatite mineralization remains unknown. Here, combining nanometre-scale resolution cryogenic transmission electron microscopy and cryogenic electron tomography with molecular modelling, we show that collagen functions in synergy with inhibitors of hydroxyapatite nucleation to actively control mineralization. The positive net charge close to the C-terminal end of the collagen molecules promotes the infiltration of the fibrils with amorphous calcium phosphate (ACP). Furthermore, the clusters of charged amino acids, both in gap and overlap regions, form nucleation sites controlling the conversion of ACP into a parallel array of oriented apatite crystals. We developed a model describing the mechanisms through which the structure, supramolecular assembly and charge distribution of collagen can control mineralization in the presence of inhibitors of hydroxyapatite nucleation.     

Creep Deformation and the Grain-Boundary Resistivity of Tetragonal Zirconia Polycrystalline Materials

The grain-boundary resistivity of tetragonal zirconia polycrystals, which had undergone creep with different applied compressive loads and at different temperatures, has been measured with impedance spectroscopy. A stress exponent of unity was determined from strain rate versus stress data. The grain-boundary resistivity decreased significantly with increasing stress at a constant creep temperature indicating squeezing out of the glassy phase from interfaces between grains. This, however, had no effect on the activation energy for the grain-boundary resistivity.     

Methods of non-linear stochastic dynamics for the assessment of structural fragility

Some of the external events which can significantly contribute to the overall risk of a nuclear power plant, give rise to a dynamic excitation of the structural components which form the plant. The computation of the risk associated with these external events requires an investigation of the behaviour of the structural components beyond the elastic limit. The stochastic nature of the excitation, then, leads one to deal with a non-linear stochastic dynamic problem.

No general method of solution exists for such a problem when large structural systems are considered, although classical methods of propagating uncertainty have been successfully employed. This paper investigates the possibility of formulating an approach founded on a suitable equivalent linearization technique. In particular the authors make operative a new method of fragility analysis to be applied directly to the linearized system.

The numerical example considers a framed structural component: its aim is to show the degree of accuracy that can be reached by the approach formulated in the paper.     

Computer-assisted preoperative planning of a novel design of total ankle replacement

Computer-assisted preoperative planning are particularly sought for enhancing surgical implantation and improving clinical outcome of ankle joint replacement arthroplasty. A planning tool was designed to enhance the surgical implantation of a novel ligament-compatible three-component prosthesis. The tool includes a geometric and mechanical model of the ankle complex. The geometry of the articular surfaces and of main ligaments is defined from digitisation on a scanned lateral radiographs of the joint. From the expected movement arc and from component dimensions, a mechanical model of the ligaments is used to select the optimal size and the position the three prosthesis components. The planning is based on the minimisation of ligament change in length and of bone stock to be removed. The main output report is a detailed picture of the replaced ankle with indications of component locations and quantitative measure of bone cuts. An advanced report also superimposes the resulting prediction of prosthesis component kinematics at the replaced joint to the original radiographs.     

Controlling and investigating cellular automaton behavior via interactive inversion and visualization of the search space

An interactive genetic algorithm (IGA) provides a means to optimize the input parameters controlling the behavior of a cellular automaton (CA). The result is one or more combinations of parameters that allow the CA to reproduce geological patterns of fluid flow and chemical reactions in fractured media. Via the IGA, the user can provide subjective feedback on the quality of the CA results, which would otherwise be difficult to express numerically. A simple modification to the IGA ranking process, combined with a self-organizing map, enables the rapid on-line visualization of the highdimensional parameter space, and consequent control over the inversion itself. The insights into the topology of the parameter space offer an understanding of which parameters control different CA behaviors. Fabio Boschetti, Ph.D.: He obtained his Ph.D. in Mathematical Geophysics at the University of Western Australia in 1996. His main expertise lies in global optimisation, evolutionary computation, image processing, visualisation of multivariate data and human-computer interaction. Currently he works for CSIRO, Australia, focussing on forward and inverse modelling of geological processes.