Non-destructive measurement of cavity pressure during injection molding process based on ultrasonic technology and Gaussian process

Non-destructive measurement of the cavity pressure is of great importance for monitoring, optimizing and controlling the injection molding process. However, to date, almost all researches have relied on embedded pressure probes, and holes have to be drilled in the molds. In this paper, a non-destructive cavity pressure measurement method is proposed based on ultrasonic technology and a Gaussian process. According to the pressure-volume-temperature profile, the cavity pressure of a given polymer can be treated as a function of the density and the temperature. Moreover, the cavity pressure is significantly affected by injection hydro-cylinder pressure. Ultrasonic technology is employed to detect the variation of polymer density during injection molding. The Gaussian process is adopted to model the functional relationships between the cavity pressure, the ultrasonic signal, the mold temperature and the injection hydro-cylinder pressure. Experimental results show that the proposed Gaussian process regression model has a better modeling performance than that of the neural network regression model, and the proposed measurement method is capable of measuring the cavity pressure at different processing conditions and measurement locations during injection molding. In general, the proposed method offers several advantages: (1) non-destructive, (2) flexible, (3) no wires, (4) low-cost, and (5) health and safety, so it has great application prospects in injection molding.     

Additivity of statistical moments in the exponentially modified Gaussian model of chromatography

A homologous series of saturated fatty acids ranging from C₁₀ to C₂₂ was separated by reversed-phase capillary liquid chromatography. The resultant zone profiles were found to be fit best by an exponentially modified Gaussian (EMG) function. To compare the EMG function and statistical moments for the analysis of the experimental zone profiles, a series of simulated profiles was generated by using fixed values for retention time and different values for the symmetrical (σ) and asymmetrical (τ) contributions to the variance. The simulated profiles were modified with respect to the integration limits, the number of points, and the signal-to-noise ratio. After modification, each profile was analyzed by using statistical moments and an iteratively fit EMG equation. These data indicate that the statistical moment method is much more susceptible to error when the degree of asymmetry is large, when the integration limits are inappropriately chosen, when the number of points is small, and when the signal-to-noise ratio is small. The experimental zone profiles were then analyzed by using the statistical moment and EMG methods. Although care was taken to minimize the sources of error discussed above, significant differences were found between the two methods. The differences in the second moment suggest that the symmetrical and asymmetrical contributions to broadening in the experimental zone profiles are not independent. As a consequence, the second moment is not equal to the sum of σ² and τ², as is commonly assumed. This observation has important implications for the elucidation of thermodynamic and kinetic information from chromatographic zone profiles.     

Scoring multiple features to predict drug disease associations using information fusion and aggregation

Prediction of drug–disease associations is one of the current fields in drug repositioning that has turned into a challenging topic in pharmaceutical science. Several available computational methods use network-based and machine learning approaches to reposition old drugs for new indications. However, they often ignore features of drugs and diseases as well as the priority and importance of each feature, relation, or interactions between features and the degree of uncertainty. When predicting unknown drug–disease interactions there are diverse data sources and multiple features available that can provide more accurate and reliable results. This information can be collectively mined using data fusion methods and aggregation operators. Therefore, we can use the feature fusion method to make high-level features. We have proposed a computational method named scored mean kernel fusion (SMKF), which uses a new method to score the average aggregation operator called scored mean. To predict novel drug indications, this method systematically combines multiple features related to drugs or diseases at two levels: the drug–drug level and the drug–disease level. The purpose of this study was to investigate the effect of drug and disease features as well as data fusion to predict drug–disease interactions. The method was validated against a well-established drug–disease gold-standard dataset. When compared with the available methods, our proposed method outperformed them and competed well in performance with area under cover (AUC) of 0.91, F-measure of 84.9% and Matthews correlation coefficient of 70.31%.     

Kohn-Sham potentials corresponding to Slater and Gaussian basis set densities

A new method based on linear response theory is proposed for the determination of the Kohn-Sham potential corresponding to a given electron density. The method is very precise and affords a comparison between Kohn-Sham potentials calculated from correlated reference densities expressed in Slater-(STO) and Gaussian-type orbitals (GTO). In the latter case the KS potential exhibits large oscillations that are not present in the exact potential. These oscillations are related to similar oscillations in the local error function δ _i (r)=(−ɛ _i )ϕ _i (r) when SCF orbitals (either Kohn-Sham or Hartree-Fock) are expressed in terms of Gaussian basis functions. Even when using very large Gaussian basis sets, the oscillations are such that extreme care has to be exercised in order to distinguish genuine characteristics of the KS potential, such as intershell peaks in atoms, from the spurious oscillations. For a density expressed in GTOs, the Laplacian of the density will exhibit similar spurious oscillations. A previously proposed iterative local updating method for generating the Kohn-Sham potential is evaluated by comparison with the present accurate scheme. For a density expressed in GTOs, it is found to yield a smooth “average” potential after a limited number of cycles. The oscillations that are peculiar to the GTO density are constructed in a slow process requiring very many cycles. Received: 24 February 1997 / Accepted: 18 June 1997     

Methods of calculating protein hydrophobicity and their application in developing correlations to predict hydrophobic interaction chromatography retention

Hydrophobic interaction chromatography (HIC) is a key technique for protein separation and purification. Different methodologies to estimate the hydrophobicity of a protein are reviewed, which have been related to the chromatographic behavior of proteins in HIC. These methodologies consider either knowledge of the three-dimensional structure or the amino acid composition of proteins. Despite some restrictions; they have proven to be useful in predicting protein retention time in HIC.     

Retention model of protein for mixedmode interaction mechanism in ion exchange and hydrophobic interaction chromatography

A unified retention equation of proteins was proved to be valid for a mixed-mode interaction mechanism in ion exchange chromatography (IEC) and hydrophobia interaction chro-matography (HIC). The reason to form a "U" shape retention curve of proteins hi both HIC and IEC was explained and the concentration range of the strongest elution ability for the mobile phase was determined with this equation. The parameters in this equation could be used to characterize the difference for either HIC or IEC adsorbents and the changes in the molecular conformation of proteins. With the parameters in this equation, the contributions of salt and water in the mobile phase to the protein retention in HIC and IEC were discussed, respectively. In addition, the comparison between the unified equation and Melander' s three-parameter equation for mixed-mode interaction chromatography was also investigated and better results were obtained in former equation.     

Comparative study to predict toxic modes of action of phenols from molecular structures

Quantitative structure–activity relationship models for the prediction of mode of toxic action (MOA) of 221 phenols to the ciliated protozoan Tetrahymena pyriformis using atom-based quadratic indices are reported. The phenols represent a variety of MOAs including polar narcotics, weak acid respiratory uncouplers, pro-electrophiles and soft electrophiles. Linear discriminant analysis (LDA), and four machine learning techniques (ML), namely k-nearest neighbours (k-NN), support vector machine (SVM), classification trees (CTs) and artificial neural networks (ANNs), have been used to develop several models with higher accuracies and predictive capabilities for distinguishing between four MOAs. Most of them showed global accuracy of over 90%, and false alarm rate values were below 2.9% for the training set. Cross-validation, complementary subsets and external test set were performed, with good behaviour in all cases. Our models compare favourably with other previously published models, and in general the models obtained with ML techniques show better results than those developed with linear techniques. We developed unsupervised and supervised consensus, and these results were better than our ML models, the results of rule-based approach and other ensemble models previously published. This investigation highlights the merits of ML-based techniques as an alternative to other more traditional methods for modelling MOA.     

On the development of an effective model potential to describe water interaction in neutral and ionic clusters

Various effective components of the intermolecular interaction of water containing aggregates are examined and their modeling, in terms of the fundamental physical properties of the involved partners, is discussed. We focus, in particular, on the evolution of these components in going from the simplest neutral rare gas–water aggregates to bulk water and ionic water solutions. The analysis singled out that the model chosen to represent the van der Waals interaction as the composition of the action of three dispersion/induction–attraction centres and found to be appropriate to describe the lighter He–H₂O and Ne–H₂O systems, is not adequate to describe the heavier Ar–H₂O aggregate. It was found, instead, that by increasing the mass of the rare gas, other short range contributions to the interaction come into play. Moreover, it was also found that the water molecule tends to behave as a single centre as the strength of the interaction increases. This led to the development of an effective model potential suitable to describe water clusters in the range going from gaseous to condensed phase. The role of electrostatic contributions is also evaluated. The proposed potential model is tested by comparing molecular beam scattering and neutron diffraction experiments with results of molecular dynamics (MD) calculations.