GMTR: two-dimensional geo-fit multitarget retrieval model for michelson interferometer for passive atmospheric sounding/environmental satellite observations

We present a new retrieval model designed to analyze the observations of the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS), which is on board the ENVironmental SATellite (ENVISAT). The new geo-fit multitarget retrieval model (GMTR) implements the geo-fit two-dimensional inversion for the simultaneous retrieval of several targets including a set of atmospheric constituents that are not considered by the ground processor of the MIPAS experiment. We describe the innovative solutions adopted in the inversion algorithm and the main functionalities of the corresponding computer code. The performance of GMTR is compared with that of the MIPAS ground processor in terms of accuracy of the retrieval products. Furthermore, we show the capability of GMTR to resolve the horizontal structures of the atmosphere. The new retrieval model is implemented in an optimized computer code that is distributed by the European Space Agency as "open source" in a package that includes a full set of auxiliary data for the retrieval of 28 atmospheric targets.     

Modeling of the polluting emissions from a cement production plant by partial least-squares, principal component regression, and artificial neural networks

A Portland cement process was taken into consideration and monitored for one month with respect to polluting emissions, fuel and raw material physical-chemical properties, and operative conditions. Soft models, based on linear (partial least-squares, PLS, and principal component regression, PCR) and nonlinear (artificial neural networks, ANNs) approaches, were employed to predict the polluting emissions. The predictive ability of the three regression methods was evaluated by means of the partition of the dataset by Kohonen self-associative maps into both a training and a test set. Then, a "leave-more-out" approach, based on the use of a training set, a test set, and a production set, was adopted. The training set was used to build the models, the test set was used to select the number of latent variables or the neural network training endpoint, and the production set was used to produce genuine predictions. ANNs proved to be much more effective in prediction with respect to PLS and PCR and, at least in the case of SO2 and dust, provided a predictive ability comparable with the experimental estimated uncertainty of the response. This showed that it is possible to satisfactorily predict the two responses. Such a prediction will result in the prevention of environmental and legal problems connected to the polluting emissions.     

Finite-element modeling of bones from CT data: sensitivity to geometry and material uncertainties

The aim of this paper is to analyze how the uncertainties in modelling the geometry and the material properties of a human bone affect the predictions of a finite-element model derived from computed tomography (CT) data. A sensitivity analysis, based on a Monte Carlo method, was performed using three femur models generated from in vivo CT datasets, each subjected to two different loading conditions. The geometry, the density and the mechanical properties of the bone tissue were considered as random input variables. Finite-element results typically used in biomechanics research were considered as statistical output variables, and their sensitivity to the inputs variability assessed. The results showed that it is not possible to define a priori the influence of the errors related to the geometry definition process and to the material assignment process on the finite-element analysis results. The errors in the geometric representation of the bone are always the dominant variables for the stresses, as was expected. However, for all the variables, the results seemed to be dependent on the loading condition and to vary from subject to subject. The most interesting result is, however, that using the proposed method to build a finite-element model of a femur from a CT dataset of the quality typically achievable in the clinical practice, the coefficients of variation of the output variables never exceed the 9%. The presented method is hence robust enough to be used for investigating the mechanical behavior of bones with subject-specific finite-element models derived from CT data taken in vivo.     

Preparation and Characterization of Hyperbranched Polymer/Silica Hybrid Nanocoatings by Dual‐Curing Process

Summary: Organic‐inorganic nanocomposite hybrid coatings were prepared through a dual‐cure process involving cationic photopolymerization of a hyperbranched epoxy functionalized resin and subsequent condensation of an alkoxysilane inorganic precursor. All the formulations investigated gave rise to photocured films characterized by high gel content values. An increase in glass transition temperature and an increase in storage modulus above T_g in the rubbery plateau is observed with increasing TEOS content in the photocurable formulation. The important role of GPTS on reducing the inorganic domain size and avoiding macroscopic phase separation was demonstrated by TEM analyses.

TEM obtained for one of the cured films in the presence of GPTS.     

FEMMINA real-time, radio-frequency echo-signal equipment for testing novel investigation methods

Fast echographic multiparameter multi-image novel apparatus (FEMMINA), is a hardware and software platform dedicated to ultrasonic signal and image processing. FEMMINA is able to operate with sequences of radiofrequency (RF) frames. Its architecture is designed to be modular, expandable, and aimed at implementing different ultrasonic investigation techniques. The first experimental characteristic of this system is in its capability to operate in real time with ultrasonic RF signals, starting from acquisition up to processing, storage, and visualization. The second characteristic is the user-system interactivity that allows one to modify the operation appropriately while observing results. Currently, FEMMINA works in both typical experimental situations to study novel investigation techniques and clinical field to validate the proposed methods in different human districts.     

Power efficiency evaluation in Dickson and voltage doubler charge pump topologies

This paper presents a theoretical and experimental comparison between two charge pump architectures commonly used in CMOS integrated circuits, namely the Dickson scheme and the cascade of voltage doublers. The comparison is carried out considering power efficiency as the main feature of interest. To compare the two topologies, two charge pumps were integrated in 0.18-μm triple-well CMOS technology. The two charge pumps were designed with the same operating clock frequency, the same storage capacitance per stage, and the same number of stages (and, thus, approximately the same area). The theoretical and the experimental comparison showed that the power efficiency of the voltage doubler scheme is higher (by about 13% at ), mainly thanks to the lower parasitic capacitance associated to the boosted nodes.     

Sample treatment considerations in the analysis of organic colorants by surface-enhanced Raman scattering

The introduction of surface-enhanced Raman spectroscopy (SERS) in the field of cultural heritage has significantly improved the analysis of the organic dyes and their complexes that have been used as textile dyes and pigments in paintings and other polychrome works of art since antiquity. Over the last five years, a number of different procedures have been developed by various research groups. In this Article, we evaluate the effect of pretreating samples by exposing them to hydrofluoric acid (HF) vapor prior to SERS analysis, a step designed to hydrolyze the dye-metal complexes and increase analyte adsorption on the nanosized metallic support, thus enhancing the SERS signal. Materials studied include pure colorants, commercial lake pigments, and fibers from dyed textiles, as well as actual aged samples, such as microscopic fragments of lakes on paper and ancient pigments and glazes from several works of art, covering a wide range of time, from the second century B.C. to the early 20th century. In each case, SERS spectra obtained with or without HF hydrolysis were critically evaluated. The pretreatment with HF vapor resulted in faster analysis and increased sensitivity in most cases, with the exception of dyed silk fibers, where silk protein hydrolyzates were found to interfere with SERS analysis. As a final point, a two-step procedure including SERS on untreated and treated samples is proposed as a standard approach: by analyzing a sample first without hydrolysis, and then, following removal of the colloid, upon HF treatment, the best and most reliable results for a great number of dyes and substrates are assured.     

Construction of xylose dehydrogenase displayed on the surface of bacteria using ice nucleation protein for sensitive D-xylose detection

A novel method was developed to detect d-xylose (INS 967) sensitively and selectively, which is based on a xylose dehydrogenase (XDH) cell-surface displaying system using a newly identified ice nucleation protein from Pseudomonas borealis DL7 as an anchoring motif. With coenzyme NAD(+), the XDH-displayed bacteria facilitates the catalysis of the oxidization of xylose and the resultant NADH can be detected spectrometrically at 340 nm. The fusion protein was characterized by proteinase accessibility, Western blot, and enzyme activity assays. The established XDH surface displaying system did not inhibit the growth of the recombinant Escherichia coli strain. The XDH was mainly displayed on the surface of host cells, which is of high XDH activity and high D-xylose specificity. The optimal temperature and pH of cell displayed XDH were found at 30 °C and pH 8.0, respectively. The XDH-displayed bacteria can be used directly without further enzyme extraction and purification, and it improved the stability of the enzyme. Moreover, the cell-surface-displayed-protein-based approach showed a wide linear range (5-900 μM) and a low detection limit of 2 μM of d-xylose. More importantly, the recombinant cells could be used for precise detection of D-xylose from the real samples such as foods and degradation products of lignocellulose. The method shown here provides a simple, rapid, and low-cost strategy for the sensitive and selective measurement of D-xylose. In addition, the XDH-displayed bacteria showed an interesting response in developing electrochemical biosensors. Thus, the genetically engineered cells may find broad application in such biosensors and biocatalysts. Similarly, this type of genetic approach may be used for the expression of other intracellular enzymes of interest for certain purposes.     

How To Find a Needle (or Anything Else) in a Haystack: Two-Dimensional Correlation Spectroscopy-Filtering with Iterative Random Sampling Applied to Pharmaceutical Heparin

Risks of contamination of the major clinical anticoagulant heparin can arise from deliberate adulteration with unnatural or natural polysaccharides, including heparin from other animal sources, other natural products, or artifacts of manufacture, and these can escape detection by conventional means. Currently, there is no generally applicable, objective test recommended by regulators that can detect these in pharmaceutical heparin, and this continues to leave heparin exposed to contamination risks. Two-dimensional correlation spectroscopic-filtering with iterative random sampling (2D-COS-firs) is reported. It employs a difference covariance matrix with iterative random sampling, and is capable of revealing contamination in pharmaceutical heparin to a high level of sensitivity irrespective of the nature of those features. The technique is suitable to any situation in which a comparison of a single entity to a family of heterogeneous entities, particularly natural products and biosimilars, needs to be made, and will find application in pharmaceutical monitoring, manufacturing quality control, materials science, biotechnology, and metabolomic investigations.