Self-locking analysis in closed kinematic chains

Self-locking analysis in closed kinematic chains is sometimes likened to kinematic singularity analysis, especially when mechanisms are characterized by more than one degree of freedom. Although in singular configurations a mechanism is obviously locked-up since joint constraint reactions and friction forces rise to infinity, this approach identifies only a condition sufficient for self-locking, while the phenomenon actually occurs in a larger domain, the size of which depends on the values of friction coefficients.The paper proposes a definition of self-locking for multi degrees of freedom mechanisms and presents an algorithm for computing the geometrical locus that corresponds to a specific self-locking configuration. This methodology is then demonstrated on a simple parallel kinematic mechanism with two degrees of freedom.     

Effect of drying temperature on polyphenolic content and antioxidant activity of apricots

This study was carried out in order to check for the influence of drying parameters on the phenolic compounds and antioxidant activity on two apricot cultivars (Pelese and Cafona) using two sets of air drying temperatures: (1) air temperature at 55 °C; (2) air temperature at 75 °C. Whole fresh and dried fruits were assessed for: phenolics, ascorbic acid, antioxidant activity and redox potential (all parameters were calculated on a dry matter basis). Analysis of data shows that the decrease in chlorogenic and neochlorogenic acid in Cafona cultivar is higher at the lower drying temperature. Catechin showed the same behaviour of hydroxycinnamic acids in both cultivars, while the decrease in the other compounds was significantly more marked in the sample dried at 75 °C. The antioxidant activity increased significantly in Cafona fruits and this increase was confirmed by a diminution of the redox potential.     

High Glucose Exposure Impairs L-Cell Differentiation in Intestinal Organoids: Molecular Mechanisms and Clinical Implications

Intestinal organoids are used to analyze the differentiation of enteroendocrine cells (EECs) and to manipulate their density for treating type 2 diabetes. EEC differentiation is a continuous process tightly regulated in the gut by a complex regulatory network. However, the effect of chronic hyperglycemia, in the modulation of regulatory networks controlling identity and differentiation of EECs, has not been analyzed. This study aimed to investigate the effect of glucotoxicity on EEC differentiation in small intestinal organoid platforms. Mouse intestinal organoids were cultured in the presence/absence of high glucose concentrations (35 mM) for 48 h to mimic glucotoxicity. Chronic hyperglycemia impaired the expression of markers related to the differentiation of EEC progenitors (Ngn3) and L-cells (NeuroD1), and it also reduced the expression of Gcg and GLP-1 positive cell number. In addition, the expression of intestinal stem cell markers was reduced in organoids exposed to high glucose concentrations. Our data indicate that glucotoxicity impairs L-cell differentiation, which could be associated with decreased intestinal stem cell proliferative capacity. This study provides the identification of new targets involved in new molecular signaling mechanisms impaired by glucotoxicity that could be a useful tool for the treatment of type 2 diabetes.     

Sleep-related car crashes: risk perception and decision-making processes in young drivers

The aim of the present study is to analyse factors affecting worries, coping strategies and decisions of young drivers regarding the risk of sleep-related car crashes. Furthermore, the study also analyses whether framing the same information about sleepiness in two different linguistic forms influences: (1) the evaluation of the level of risk associated to a specific level of drowsiness (Attribute Framing problem); (2) the willingness to enact strategies to "prevent" sleepiness before night-time driving (Goal Framing problem); (3) the choice between two different ways, both of equal expected efficacy, of lowering drowsiness (Risky decision-making Framing problem). Six hundred and ninety-five young drivers [(57.6% females, 42.4% males); mean age 20.85 years (S.D.=1.2)] answered questions on drive risk perception and sleepiness, on nocturnal driving experience and on the strategies to deal with driver sleepiness, responding to one of the two different versions of the framed problems. A sub-sample of 130 participants completed the framed problems in both versions. The results show that experiences of sleep attacks and nocturnal driving frequency in the past 6 months affect both risk perception and the preventive strategies adopted. Furthermore, the manipulation on two out of the three problems (attribute and risky decision-making frames) significantly affected the respondents' evaluation.     

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.     

An efficient method for the synthesis of peptide aldehyde libraries employed in the discovery of reversible SARS coronavirus main protease (SARS-CoV Mpro) inhibitors

A method for the parallel solid-phase synthesis of peptide aldehydes has been developed. Protected amino acid aldehydes obtained by the racemization-free oxidation of amino alcohols with Dess-Martin periodinane were immobilized on threonyl resins as oxazolidines. Following Boc protection of the ring nitrogen to yield the N-protected oxazolidine linker, peptide synthesis was performed efficiently on this resin. A peptide aldehyde library was designed for targeting the SARS coronavirus main protease, SARS-CoV M(pro)(also known as 3CL(pro)), on the basis of three different reported binding modes and supported by virtual screening. A set of 25 peptide aldehydes was prepared by this method and investigated in inhibition assays against SARS-CoV M(pro). Several potent inhibitors were found with IC(50) values in the low micromolar range. An IC(50) of 7.5 muM was found for AcNSTSQ-H and AcESTLQ-H. Interestingly, the most potent inhibitors seem to bind to SARS-CoV M(pro) in a noncanonical binding mode.