Glaucoma is an ocular disease featuring increased intraocular pressure (IOP) and its primary treatment strategy is to lower IOP by medication. Current ocular drug delivery in treating glaucoma is confronting a variety of challenges, such as low corneal permeability and bioavailability due to the unique anatomical structure of the human eye. To tackle these challenges, a cubosome drug delivery system for glaucoma treatment was constructed for timolol maleate (TM) in this study. The TM cubosomes (liquid crystalline nanoparticles) were prepared using glycerol monooleate and poloxamer 407 via high-pressure homogenization. These constructed nanoparticles appeared spherical using transmission electron microscopy and had an average particle size of 142 nm, zeta potential of -6.27 mV, and over 85% encapsulation efficiency. Moreover, using polarized light microscopy and small-angle X-ray scattering (SAXS), it was shown that the TM cubosomes have cubic liquid crystalline D-type (Pn3m) structure, which provides good physicochemical stability and high encapsulation efficiency. Ex vivo corneal permeability experiments showed that the total amount of TM cubosomes penetrated was higher than the commercially available eye drops. In addition, in vivo studies revealed that TM cubosomes reduced the IOP in rabbits from 27.8∼39.7 to 21.4∼32.6 mmHg after 1-week administration and had a longer retention time and better lower-IOP effect than the commercial TM eye drops. Furthermore, neither cytotoxicity nor histological impairment in the rabbit corneas was observed. This study suggests that cubosomes are capable of increasing the corneal permeability and bioavailability of TM and have great potential for ocular disease treatment.

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Numerous practical applications of the Discrete Element Method (DEM) require a flexible description of particles that can account for irregular and non-convex particle shape features. Capturing the particle non-convexity is important since it allows to model the physical interlocking when the particles are in contact. To that end, the most flexible approach to capture the particle shape is via a polyhedron, which provides a faceted representation of any shape, albeit at a significant computational cost. In this study we present a decomposition approach to modeling non-convex polyhedral particles as an extension of an existing open source convex polyhedral discrete element code, BlazeDEM-GPU, which computes using general purpose graphical processing units (GPGPUs). Although the principle of decomposition of non-convex particles into convex particles is not new, its application by the discrete element modeling community has been rather limited. The non-convex extension of BlazeDEM-GPU was validated using a hopper flow experiment with identical convex and identical non-convex 3D printed particles. The experiment was designed around two sensitive flow points, with the convex particles following the intermittent flow and the nonconvex particles forming stable arches. It was demonstrated that the DEM simulations can be applied to reproduce both the convex and the non-convex flow behavior using the same parameter set. This study is a significant step towards general computing of non-convex particles for industrial-scale applications using the GPGPUs.

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Abstract:The rapid expansion of wind farms has accelerated research into improving the reliability of wind turbines to reduce operational and maintenance costs. A critical component in wind turbine drive-trains is the gearbox, which is prone to different types of failures due to long-term operation under tough environments, variable speeds and alternating loads. To detect gearbox fault early, a method is proposed for an effective fault diagnosis by using improved ensemble empirical mode decomposition (EEMD) and Hilbert square demodulation (HSD). The method was verified numerically by implementing the scheme on the vibration signals measured from bearing and gear test rigs. In the implementation process, the following steps were identified as being important: (1) in order to increase the accuracy of EEMD, a criterion of selecting the proper resampling frequency for raw vibration signals was developed; (2) to select the fault related intrinsic mode function (IMF) that had the biggest kurtosis index value, the resampled signal was decomposed into a series of IMFs; (3) the selected IMF was demodulated by means of HSD, and fault feature information could finally be obtained. The experimental results demonstrate the merit of the proposed method in gearbox fault diagnosis.

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