Experimental Investigation of Laminar Convection Heat Transfer of Al2O3-Ethylene Glycol-Water Nanofluid as a Coolant in a Car Radiator

In this experimental study, heat transfer of a coolant nanofluid, obtained by adding alumina nanoparticles to Ethylene Glycol-water (60:40 by mass), in a car radiator has been investigated. For this purpose, an experimental setup has been designed and constructed. Firstly, to investigate the accuracy of the results, the experiments have been done for the base fluid. Then the experiments have been performed for the nanofluid with different nanoparticles volume fractions of 0.003, 0.006, 0.009 and 0.012. To ensure laminar flow regime three coolant flow rates of 9, 11 and 13 lit/min have been tested. The thermophysical properties have been calculated using the recently presented temperature dependent models in the literature. According to the results, both the convective heat transfer coefficient and Nusselt number increase (about 9%) with increasing the coolant flow rate. Also, convective heat transfer coefficient increases with increasing the nanoparticles volume fraction. Although Nusselt number decreases when nanofluid is utilized, it enhances as the nanoparticles volume fraction increases. Based on the experimental results obtained, A new empirical correlation has been developed for average Nusselt number of Al2O3-EG-water nanofluid in developing region of flat tubes of car radiator for laminar flow and its maximum error is 3%

The Effect of Volume Fraction of Single-Walled Carbon Nanotubes on Natural Frequencies of Polymer Composite Cone-Shaped Shell Made from Poly(Methyl Methacrylate)

In this paper, the effect of volume fraction of single-walled carbon nanotubes on natural frequencies of polymer composite cone-shaped shells made from Poly(Methyl Methacrylate) (PMMA) is studied. In order to determine the characterization of materials reinforced with nanoparticles, the molecular dynamics and mixture rule has been used. The motion equations of composite shell based on the classical thin shells theory using Hamilton’s principle are obtained. Then, using the Ritz method, approximate analytical solution of the natural frequency is presented. Results indicate that the nanotubes have a noticeable effect on the natural frequencies

AP-4-mediated axonal transport controls endocannabinoid production in neurons

Davies et al. identify a putative mechanism underlying the childhood neurological disorder AP-4 deficiency syndrome. In the absence of AP-4, an enzyme that makes 2-AG is not transported to the axon, leading to axonal growth defects, which can be rescued by inhibition of 2-AG breakdown. The adaptor protein complex AP-4 mediates anterograde axonal transport and is essential for axon health. AP-4-deficient patients suffer from a severe neurodevelopmental and neurodegenerative disorder. Here we identify DAGLB (diacylglycerol lipase-beta), a key enzyme for generation of the endocannabinoid 2-AG (2-arachidonoylglycerol), as a cargo of AP-4 vesicles. During normal development, DAGLB is targeted to the axon, where 2-AG signalling drives axonal growth. We show that DAGLB accumulates at the trans-Golgi network of AP-4-deficient cells, that axonal DAGLB levels are reduced in neurons from a patient with AP-4 deficiency, and that 2-AG levels are reduced in the brains of AP-4 knockout mice. Importantly, we demonstrate that neurite growth defects of AP-4-deficient neurons are rescued by inhibition of MGLL (monoacylglycerol lipase), the enzyme responsible for 2-AG hydrolysis. Our study supports a new model for AP-4 deficiency syndrome in which axon growth defects arise through spatial dysregulation of endocannabinoid signalling.Special thanks to the MPIB Imaging Facility for outstanding technical support, in particular to Giovanni Cardone for his advice and assistance with the implementation of image analysis pipelines, as well as feedback on the manuscript, and to Martin Spitaler for his expert technical advice for imaging experiments

Drug-Carrying Magnetic Nanocomposite Particles for Potential Drug Delivery Systems

Drug-carrying magnetic nanocomposite spheres were synthesized using magnetite nanoparticles and poly (D,L-lactide-co-glycolide) (PLGA) for the purpose of magnetic targeted drug delivery. Magnetic nanoparticles (∼13 nm on average) of magnetite were prepared by a chemical coprecipitation of ferric and ferrous chloride salts in the presence of a strong basic solution (ammonium hydroxide). An oil-in-oil emulsion/solvent evaporation technique was conducted at 7000 rpm and 1.5–2 hours agitation for the synthesis of nanocomposite spheres. Specifically, PLGA and drug were first dissolved in acetonitrile (oily phase I) and combined with magnetic nanoparticles, then added dropwise into viscous paraffin oil combined with Span 80 (oily phase II). With different contents (0%, 10%, 20%, and 25%) of magnetite, the nanocomposite spheres were evaluated in terms of particle size, morphology, and magnetic properties by using dynamic laser light scattering (DLLS), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and a superconducting quantum interference device (SQUID). The results indicate that nanocomposite spheres (200 nm to 1.1 μm in diameter) are superparamagnetic above the blocking temperature near 40 K and their magnetization saturates above 5 000 Oe at room temperature

BoVW model for animal recognition: an evaluation on SIFT feature strategies

Nowadays classifying images into categories have taken a lot of interests in both research and practice. Content Based Image Retrieval (CBIR) was not successful in solving semantic gap problem. Therefore, Bag of Visual Words (BoVW) model was created for quantizing different visual features into words. SIFT detector is invariant and robust to translation, rotations, scaling and partially invariant to affine distortion and illumination changes. The aim of this paper is to investigate the potential usage of BoVW Word model in animal recognition. The better SIFT feature extraction method for pictures of the animal was also specified. The performance evaluation on several SIFT feature strategies validates that MSDSIFT feature extraction will get better results