Preparation and Evaluation of a Self-Nanoemulsifying Drug Delivery System Loaded with Heparin Phospholipid Complex

A self-nanoemulsifying drug delivery system (SNEDDS) was developed to enhance the absorption of heparin after oral administration, in which heparin was compounded with phospholipids to achieve better fat solubility in the form of heparin-phospholipid (HEP-Pc) complex. HEP-Pc complex was prepared using the solvent evaporation method, which increased the solubility of heparin in n-octanol. The successful preparation of HEP-Pc complex was confirmed by differential scanning calorimetry (DSC), Fourier-transform infrared (FT-IR) spectroscopy, NMR, and SEM. A heparin lipid microemulsion (HEP-LM) was prepared by high-pressure homogenization and characterized. HEP-LM can enhance the absorption of heparin after oral administration, significantly prolong activated partial thromboplastin time (APTT) and thrombin time (TT) in mice, and reduce fibrinogen (FIB) content. All these outcomes indicate that HEP-LM has great potential as an oral heparin formulation.     

Impact behavior of aminosilane functionalized nanosilica based shear thickening fluid impregnated Kevlar fabrics

In the present work, two types of shear thickening fluids have been synthesized by using neat and aminosilane functionalized silica nanoparticles and their viscosity curves have been obtained by the rheometer. Based on the values of peak viscosity of synthesized shear thickening fluids, the surface functionalized nanosilica based shear thickening fluid has been chosen as a best candidate due to the high viscosity for impregnation into the neat Kevlar of different layers viz. four (04) and eight (08) layers for velocity impact study. The experimental investigations reveal high energy absorption of shear thickening fluid impregnated Kevlar as compared to the neat Kevlar. The maximum energy absorption 62 J is achieved corresponding to the initial velocity 154 m∙s⁻¹ for 08 layers shear thickening fluid impregnated Kevlar specimen. The data have also been analytically determined and validated with the experimental data. The experimental data have good agreement with the analytical data within the accuracy of around 15 to 20%. The present findings can have significant inferences towards the fabrication of shear thickening fluids using nanomaterials for numerous applications such as soft armors, dampers, nanofinishing and so forth.     

Hydrophobic Cross-Linked Poly(dimethylsiloxane)-Based Sorbents for Oil Spill Applications

With the increase of industrialization and urbanization, humankind faces massive oil-based pollution due to tanker accidents, human error, and natural disasters. For this, hydrophobic sorbents are fabricated and their applications for the removal of oil from polluted water sources are investigated. These hydrophobic sorbents are prepared by the condensation reaction of poly(dimethylsiloxane) and tris[3-(trimethoxysilyl)propyl]isocyanurate cross-linker via bulk polymerization. The obtained sorbents exhibit high oil sorption capacity, fast absorption–desorption kinetics, and great reusability. Moreover, they can selectively absorb oil from the water surface, thus making them practical for water clean-up applications.     

Enhanced electromagnetic microwave absorption of Fe/C/SiCN composite ceramics targeting in integrated structure and function

The Fe/C/SiCN composite ceramics were synthesized by polymer-derived method to obtain the integration of structure and functions. The electromagnetic waves (EMW) absorption properties at X and Ku bands were investigated. The addition of nano-sized Fe particles improved the magnetic loss and impedance matching, and the carbon nanotubes generated by the iron in-situ catalysis increased the internal relaxation polarization and interfacial polarization, which together improved the EMW absorption properties significantly. In particular, the Fe/C/SiCN-9 showed the optimum reflection loss (RL) of ?31.06 dB at 10.03 GHz with an effective absorption bandwidth (EAB, RL < ?10 dB) of 3.03 GHz at 2.51 mm, indicating the excellent EMW absorption properties of Fe/C/SiCN composite ceramics.     

A self-adaptive synthetic over-sampling technique for imbalanced classification

Traditionally, in supervised machine learning, (a significant) part of the available data (usually 50%-80%) is used for training and the rest—for validation. In many problems, however, the data are highly imbalanced in regard to different classes or does not have good coverage of the feasible data space which, in turn, creates problems in validation and usage phase. In this paper, we propose a technique for synthesizing feasible and likely data to help balance the classes as well as to boost the performance in terms of confusion matrix as well as overall. The idea, in a nutshell, is to synthesize data samples in close vicinity to the actual data samples specifically for the less represented (minority) classes. This has also implications to the so-called fairness of machine learning. In this paper, we propose a specific method for synthesizing data in a way to balance the classes and boost the performance, especially of the minority classes. It is generic and can be applied to different base algorithms, for example, support vector machines, k-nearest neighbour classifiers deep neural, rule-based classifiers, decision trees, and so forth. The results demonstrated that (a) a significantly more balanced (and fair) classification results can be achieved and (b) that the overall performance as well as the performance per class measured by confusion matrix can be boosted. In addition, this approach can be very valuable for the cases when the number of actual available labelled data is small which itself is one of the problems of the contemporary machine learning.     

Automatic generation of variants depending on changes of product properties in a flexible manufacturing environment

Competition forces manufacturing systems to be flexible and to increase product variety and process complexity. These tasks depend on the flexible design of a bill of materials (BOM), one of the most important inputs in manufacturing planning and control systems. Product variety forces systems to generate BOMs with regard to product properties through a BOM pattern. A variant bill of materials provides a structure to manage product variability. In this study, an algorithm is designed to build a BOM pattern using computer-aided design and computer-aided manufacturing (CAD/CAM) data, and another algorithm is designed to generate variants with regard to product specifications. Genetic algorithm is used to generate new products to provide high product variability for testing algorithms. After the test, both algorithms are applied to a real industry problem. The BOM pattern is built automatically using CAD/CAM data, and variants are generated with regard to the pattern, and the results are discussed.     

应变纤锌矿ZnO/MgxZn1-xO量子点中离子施主束缚激子的光学性质

Within the framework of the effective-mass approximation and the dipole approximation, considering the three-dimensional confinement of the electron and hole and the strong built-in electric field(BEF) in strained wurtzite Zn O/Mg0:25Zn0:75O quantum dots(QDs), the optical properties of ionized donor-bound excitons(D+, X)are investigated theoretically using a variational method. The computations are performed in the case of finite band offset. Numerical results indicate that the optical properties of(D+, X) complexes sensitively depend on the donor position, the QD size and the BEF. The binding energy of(D+, X) complexes is larger when the donor is located in the vicinity of the left interface of the QDs, and it decreases with increasing QD size. The oscillator strength reduces with an increase in the dot height and increases with an increase in the dot radius. Furthermore, when the QD size decreases, the absorption peak intensity shows a marked increment, and the absorption coefficient peak has a blueshift. The strong BEF causes a redshift of the absorption coefficient peak and causes the absorption peak intensity to decrease remarkably. The physical reasons for these relationships have been analyzed in depth.