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Spiral bevel and hypoid gears are key components widely used for transmitting significant power in various types of vehicles and engineering machineries. In reality, these gear surfaces are quite rough with three-dimensional (3D) topography that may significantly influence the lubrication formation and breakdown as well as components failures. Previous spiral bevel and hypoid gears lubrication studies, however, were limited mostly to cases under the full-film lubrication condition with smooth surfaces. In the present study, a comprehensive analysis for gearing geometry, kinematics, mixed lubrication performance, and friction and interfacial flash temperature in spiral bevel and hypoid gears is developed based on a recently developed mixed elastohydrodynamic lubrication (EHL) model that is capable of handling practical cases with 3D machined roughness under severe operating conditions and considering the effect of arbitrary entrainment angle. Obtained results from sample cases show that the simulation model developed can be used as an engineering tool for spiral bevel and hypoid gears design optimization and strength prediction.

Rational molecular design for the organic nanocrystal morphology still remains a challenge due to the structural diversity and complicated weak intermolecular interactions. In this work, a typical attractor-repulsor molecule N,N-diphenyl-4-(9-phenyl-fluoren-9-yl) phenylamine (TPA-PF) is designed to explore a general assembly strategy for 2D nanocrystals. Via an interdigital lipid bilayer-like (ILB) molecular packing mode, large-sized lamellar 2D nanosheets are obtained with a length:width:thickness ratio as ≈2500:1000:1. The d-spacing of the largest (001) plane is 1.32 nm, which equals to the thickness of a single interdigital stacking layer. The synergetic effect of the attractive supramolecular segment (TPA) and the repulsive bulky group (PF) is supposed to be the critical factor for the ILB packing that leads to the 2D structures. The attractor-repulsor molecule design is expected to be an effective strategy for the growth of 2D nanocrystals based on small organic molecules.

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Nanotechnology presents great potential for increasing efficacy of docetaxel while reducing side-effects and toxicity. However, in vivo toxicity of nano-formulation of docetaxel has not been systemically investigated yet. Herein, the new docetaxel-loaded solid lipid nanoparticles (DSNs) were prepared, and systemic toxicity of DSNs in different animals was comprehensively investigated. The experimental results showed that no allergenicity and vascular irritation were induced by DSNs at the highest drug concentration of clinical infusion. The maximum tolerated dose (MTD) of DSNs was as high as 400 mg/kg in mice while the medial lethal dose (LD₅₀) of Taxotere® was 149.31 mg/kg. The long-term toxicity of DSNs compared with Taxotere® in beagle dogs by intravenous infusion weekly for four weeks showed that the administration of Taxotere® at 1 mg/kg brought about severe signs of toxicity such as skin flushing, vocalization and salivation. However, no abnormal reactions appeared on animals treated with DSNs at dose of 4 mg/kg. At the same dose level, DSNs induced more minor decreases in body weight gains, slighter hemotoxicity (changes in some clinical hematology and biochemistry parameters), cardiac toxicity, hepatotoxicity and myelosuppression than Taxotere®. These results could provide an important reference for developing the novel delivery system of docetaxel.

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Point cloud registration is essential for processing terrestrial laser scanning 16 (TLS) point cloud datasets. The registration precision directly in uences 17 and determines the practical usefulness of TLS surveys. However, in terms 18 of target based registration, analytical point cloud registration error models 19 employed by scanner manufactures are only suitable to evaluate target regis20 tration error, rather than point cloud registration error. This paper proposes 21 an new analytical approach called the registration error (RE) model to di22 rectly evaluate point cloud registration error. We verify the proposed model 23 by comparing RE and root mean square error (RMSE) for all points in 24 three point clouds that are approximately equivalent.

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