Enhancing Heat Transfer in Internal Combustion Engine by Applying Nanofluids

Nanofluids exhibit novel properties including significant heat transfer properties that make them potentially useful in internal combustion engine cooling. However, although there is a substantial number of mechanisms proposed, modeling works related to their enhanced thermal conductivity, systematic mechanisms, or models that are suitable for nanofluids are still lacked. With molecular dynamics simulations, thermal conductivities of nanofluids with various nanoparticles have been calculated. Influence rule of various factors for thermal conductivity of nanofluids has been studied. Through defining the ratio of thermal conductivity enhancement by nanoparticle volume fraction, Κ, the impacts of nanoparticle properties for thermal conductivity are further evaluated. Furthermore, the ratio of energetic atoms in nanoparticles, E, is proposed to be an effective criterion for judging the impact of nanoparticles for the thermal conductivity of nanofluids. Mechanisms of heat conduction enhancement are investigated by MD simulations. Altered microstructure and movements of nanoparticles in the base fluid are proposed to be the main reasons for thermal conductivity enhancement in nanofluids. Both the static and dynamic mechanisms for heat conduction enhancement in nanofluids have been considered to establish a prediction model for thermal conductivity. The prediction results of the present model are in good agreement with experimental results

Eleutheroside E inhibits doxorubicin-induced inflammation and apoptosis in rat cardiomyocytes by modulating activation of NF-κB pathway

Purpose: To identify the effects of eleutheroside E (EE) on apoptosis and   inflammation induced by doxorubicin (DOX) in H9c2 cells and to investigate the underlying mechanisms.Methods: The effect of EE on H9c2 cell viability was determined using Cell Counting Kit-8 (CCK8). EE effect on DOX-induced apoptosis and inflammation in H9c2 cells was studied by comparison between cells treated with DOX alone and DOX+EE; the relationship between EE effects and NF-κB signaling pathway was evaluated by the addition of NF-κB inhibitor PDTC. Cell apoptosis was examined by flow cytometry while IL-1β, IL-6, and TNF-α levels were determined by ELISA. The phosphorylation level of NF-κB p65 was measured by Western blot.Results: Compared with control group, cell viability was notably elevated after  treatment with 50-100 μM EE for 48 or 72 h. DOX induced higher rates of cell  apoptosis in H9c2 cells (29.5 ± 3.56 %) compared with control group (6.39 ± 0.67 %); however, with EE pretreatment (50 and 80 μM), apoptosis rate decreased to 16.8 ± 2.16 and 13.54 ± 2.08 %, respectively, which are significantly lower than that of DOX group; furthermore, the levels of IL-1β, IL-6, and TNF-α also reduced. In addition, DOX-induced phosphorylation of NF-κB p65 was suppressed by EE pretreatment (10, 50 and 80 μM) to 11.51 ± 1.25, 40.2 ± 5.17 and 52.97 ± 6.74 %, respectivelyConclusion: The results suggest that EE treatment reduced DOX-induced apoptosis and inflammation by interacting with NF-κB signaling pathway. This finding sheds some light on probable new strategies on the application of DOX for cancer treatment.Keywords: Eleutheroside E, Doxorubicin, Inflammation, Apoptosis, Cardiomyocytes, NF-κ

AF17 Facilitates Dot1a Nuclear Export and Upregulates ENaC-Mediated Na+ Transport in Renal Collecting Duct Cells

Our previous work in 293T cells and AF17-/- mice suggests that AF17 upregulates expression and activity of the epithelial Na+ channel (ENaC), possibly by relieving Dot1a-AF9-mediated repression. However, whether and how AF17 directly regulates Dot1a cellular distribution and ENaC function in renal collecting duct cells remain unaddressed. Here, we report our findings in mouse cortical collecting duct M-1 cells that overexpression of AF17 led to preferential distribution of Dot1a in the cytoplasm. This effect could be blocked by nuclear export inhibitor leptomycin B. siRNA-mediated depletion of AF17 caused nuclear accumulation of Dot1a. AF17 overexpression elicited multiple effects that are reminiscent of aldosterone action. These effects include 1) increased mRNA and protein expression of the three ENaC subunits (α, β and γ) and serum- and glucocorticoid inducible kinase 1, as revealed by real-time RT-qPCR and immunoblotting analyses; 2) impaired Dot1a-AF9 interaction and H3 K79 methylation at the αENaC promoter without affecting AF9 binding to the promoter, as evidenced by chromatin immunoprecipitation; and 3) elevated ENaC-mediated Na+ transport, as analyzed by measurement of benzamil-sensitive intracellular [Na+] and equivalent short circuit current using single-cell fluorescence imaging and an epithelial Volt-ohmmeter, respectively. Knockdown of AF17 elicited opposite effects. However, combination of AF17 overexpression or depletion with aldosterone treatment did not cause an additive effect on mRNA expression of the ENaC subunits. Taken together, we conclude that AF17 promotes Dot1a nuclear export and upregulates basal, but not aldosterone-stimulated ENaC expression, leading to an increase in ENaC-mediated Na+ transport in renal collecting duct cells

NARRATE: A Normal Assisted Free-View Portrait Stylizer

In this work, we propose NARRATE, a novel pipeline that enables simultaneously editing portrait lighting and perspective in a photorealistic manner. As a hybrid neural-physical face model, NARRATE leverages complementary benefits of geometry-aware generative approaches and normal-assisted physical face models. In a nutshell, NARRATE first inverts the input portrait to a coarse geometry and employs neural rendering to generate images resembling the input, as well as producing convincing pose changes. However, inversion step introduces mismatch, bringing low-quality images with less facial details. As such, we further estimate portrait normal to enhance the coarse geometry, creating a high-fidelity physical face model. In particular, we fuse the neural and physical renderings to compensate for the imperfect inversion, resulting in both realistic and view-consistent novel perspective images. In relighting stage, previous works focus on single view portrait relighting but ignoring consistency between different perspectives as well, leading unstable and inconsistent lighting effects for view changes. We extend Total Relighting to fix this problem by unifying its multi-view input normal maps with the physical face model. NARRATE conducts relighting with consistent normal maps, imposing cross-view constraints and exhibiting stable and coherent illumination effects. We experimentally demonstrate that NARRATE achieves more photorealistic, reliable results over prior works. We further bridge NARRATE with animation and style transfer tools, supporting pose change, light change, facial animation, and style transfer, either separately or in combination, all at a photographic quality. We showcase vivid free-view facial animations as well as 3D-aware relightable stylization, which help facilitate various AR/VR applications like virtual cinematography, 3D video conferencing, and post-production.Comment: 14 pages,13 figures https://youtu.be/mP4FV3evmy

CoT-UNet++: A medical image segmentation method based on contextual transformer and dense connection

Accurate depiction of individual teeth from CBCT images is a critical step in the diagnosis of oral diseases, and the traditional methods are very tedious and laborious, so automatic segmentation of individual teeth in CBCT images is important to assist physicians in diagnosis and treatment. TransUNet has achieved success in medical image segmentation tasks, which combines the advantages of Transformer and CNN. However, the skip connection taken by TransUNet leads to unnecessary restrictive fusion and also ignores the rich context between adjacent keys. To solve these problems, this paper proposes a context-transformed TransUNet++ (CoT-UNet++) architecture, which consists of a hybrid encoder, a dense connection, and a decoder. To be specific, a hybrid encoder is first used to obtain the contextual information between adjacent keys by CoTNet and the global context encoded by Transformer. Then the decoder upsamples the encoded features by cascading upsamplers to recover the original resolution. Finally, the multi-scale fusion between the encoded and decoded features at different levels is performed by dense concatenation to obtain more accurate location information. In addition, we employ a weighted loss function consisting of focal, dice, and cross-entropy to reduce the training error and achieve pixel-level optimization. Experimental results demonstrate that the proposed CoT-UNet++ method outperforms the baseline models and can obtain better performance in tooth segmentation

Relightable Neural Human Assets from Multi-view Gradient Illuminations

Human modeling and relighting are two fundamental problems in computer vision and graphics, where high-quality datasets can largely facilitate related research. However, most existing human datasets only provide multi-view human images captured under the same illumination. Although valuable for modeling tasks, they are not readily used in relighting problems. To promote research in both fields, in this paper, we present UltraStage, a new 3D human dataset that contains more than 2,000 high-quality human assets captured under both multi-view and multi-illumination settings. Specifically, for each example, we provide 32 surrounding views illuminated with one white light and two gradient illuminations. In addition to regular multi-view images, gradient illuminations help recover detailed surface normal and spatially-varying material maps, enabling various relighting applications. Inspired by recent advances in neural representation, we further interpret each example into a neural human asset which allows novel view synthesis under arbitrary lighting conditions. We show our neural human assets can achieve extremely high capture performance and are capable of representing fine details such as facial wrinkles and cloth folds. We also validate UltraStage in single image relighting tasks, training neural networks with virtual relighted data from neural assets and demonstrating realistic rendering improvements over prior arts. UltraStage will be publicly available to the community to stimulate significant future developments in various human modeling and rendering tasks. The dataset is available at https://miaoing.github.io/RNHA.Comment: Project page: https://miaoing.github.io/RNH

Characterization of Water-Based Anti-Corrosion and Anti-Fouling Coating in the Heat Exchanger Tube of Gas Water Heater

Due to the fouling and corrosion issues with gas water heater heat exchangers during use, a water-based coating is created using the sol-gel method and applied to the inner surface of the copper tube of the heat exchangers. The water-based coating can reduce the surface energy of copper that is oxygen-free by 82.40 % and has outstanding hydrophobicity. The coating's adhesion strength was 8.46 MPa, and its wet adhesion reduction rate was only 0.0375 MPa·d-1 when submerged in a bath of water at a constant temperature. Its water contact angle remained above 110° even after being abraded by sandpaper, demonstrating its excellent wear resistance. At a constant temperature of 45 °C, the corrosion experiment was carried out in salt, acid, and alkali solutions. The maximum corrosion rate of the coating was 3.24 × 10-2mg∙(cm2∙h)-1, which was only 20.85 % of that of the oxygen-free copper substrate. The dynamic fouling experiment shows that the coating can effectively inhibit fouling growth, prolong the fouling induction period and reduce the average fouling rate. By factoring in the fouling thermal resistance and the coating thermal resistance, the overall heat transfer coefficient of the copper tube was computed. The results demonstrate that after applying this water-based coating, the copper tube can continue to have a high heat transfer coefficient for an extended period, ensuring the continuous and effective operation of the heat exchanger and having practical application value

Determination of 4 Kinds of β-Agonists Residues in Braised Meat by Ultra Performance Liquid Chromatography-Tandem Mass Spectrometry

An ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC-MS) method was developed for the determination of four β-agonists (terbutaline, clenbuterol, ractopamine, salbutamol) in braised meat. Samples were hydrolyzed by β-glucuronidase and cleaned up by an SLS solid phase extraction column. The separation was performed on a Thermo Hypersil Gold C18 column with a gradient elution of 0.1% formic acid water and acetonitrile as mobile phases, ESI+ was used for multiple response monitoring (MRM) and quantitative analysis by internal standard method. The linear relationship of the four β-agonists was good in the concentration range of 0.5 μg/L to 9.5 μg/L, and the correlation coefficient (r) was greater than 0.9988. The limit of detection (LOD) was 0.1 μg/kg, and the limit of quantitation (LOQ) was 0.3 μg/kg. The recoveries were 87.9%~113.7% and RSDs were 1.48%~9.32% at three spiked levels (1, 5 and 9 μg/kg). In a total of 162 batches of braised meat samples, one sample of braised pig’s trotter was found to contain 1.51 μg/kg of clenbuterol and 3.65 μg/kg of ractopamine. Additionally, another sample of braised lamb was found to contain 11.5 μg/kg of clenbuterol. The method is rapid and accurate, and can be used for qualitative and quantitative determination of four β-agonists (terbutaline, clenbuterol, ractopamine, salbutamol) in braised meat