Flow structure transition in thermal vibrational convection

This study investigates the effect of vibration on the flow structure transitions in thermal vibrational convection (TVC) systems, which occur when a fluid layer with a temperature gradient is excited by vibration. Direct numerical simulations of TVC in a two-dimensional enclosed square box were performed over a range of dimensionless vibration amplitudes $0.001 \le a \le 0.3$ and angular frequencies $10^{2} \le \omega \le 10^{7}$, with a fixed Prandtl number of 4.38. The flow visualisation shows the transition behaviour of flow structure upon the varying frequency, characterising three distinct regimes, which are the periodic-circulation regime, columnar regime and columnar-broken regime. Different statistical properties are distinguished from the temperature and velocity fluctuations at the boundary layer and mid-height. Upon transition into the columnar regime, columnar thermal coherent structures are formed, in contrast to the periodic oscillating circulation. These columns are contributed by merging of thermal plumes near the boundary layer, and the resultant thermal updrafts remain at almost fixed lateral position, leading to a decrease in fluctuations. We further find that the critical point of this transition can be described nicely by the vibrational Rayleigh number $Ra_\mathrm{vib}$. As the frequency continues to increase, entering the so-called columnar-broken regime, the columnar structures are broken, and eventually the flow state becomes a large-scale circulation, characterised by a sudden increase in fluctuations. Finally, a phase diagram is constructed to summarise the flow structure transition over a wide range of vibration amplitude and frequency parameters.Comment: 14 pages, 9 figure

Cooperation-based sperm clusters mediate sperm oviduct entry and fertilization

Sperm cooperation has been observed in multiple species, yet its existence and benefit for reproductive success in mammals remains underexplored. Here, combining tissue-clearing with deep three-dimensional imaging, we demonstrate that postcopulatory mouse sperm congregate into unidirectional sperm cooperative clusters at the utero-tubal junction (UTJ), a key physical barrier for passage into the oviduct. Reducing sperm number in male mice by unilateral vasoligation or busulfan-treatment impairs sperm cluster formation and oviduct entry. Interestingly, sperm derived from Tex101-/- mouse has normal number, motility and morphology, yet they cannot form sperm cluster and fail to pass through the UTJ, which is at least in part due to the altered tail beating pattern of the Tex101-/- sperm. Moreover, Tex101-/- sperm's defect in oviduct entry cannot be rescued by the presence of wild-type (WT) sperm in the same uteri by sequential mating, suggesting sperm cooperative cluster as an essential behavior contributing to male fertility, which could be related to human infertility or subfertility

Kernel Negative ε Dragging Linear Regression for Pattern Classification

Linear regression (LR) and its variants have been widely used for classification problems. However, they usually predefine a strict binary label matrix which has no freedom to fit the samples. In addition, they cannot deal with complex real-world applications such as the case of face recognition where samples may not be linearly separable owing to varying poses, expressions, and illumination conditions. Therefore, in this paper, we propose the kernel negative ε dragging linear regression (KNDLR) method for robust classification on noised and nonlinear data. First, a technique called negative ε dragging is introduced for relaxing class labels and is integrated into the LR model for classification to properly treat the class margin of conventional linear regressions for obtaining robust result. Then, the data is implicitly mapped into a high dimensional kernel space by using the nonlinear mapping determined by a kernel function to make the data more linearly separable. Finally, our obtained KNDLR method is able to partially alleviate the problem of overfitting and can perform classification well for noised and deformable data. Experimental results show that the KNDLR classification algorithm obtains greater generalization performance and leads to better robust classification decision

Microstructural evolution and mechanical property development with nickel addition in low-carbon weld butt joints

Four metal powder flux-cored wires with added nickel (Ni) were designed to systematically investigate how Ni influences the microstructure and toughness in low-carbon bainitic weld metals (WMs). The microstructure was safely predicted by utilizing reasonable welding craft parameters (i.e., appropriate heat input and proper interpass temperature). Microstructural characterization, crystallographic analysis, microhardness mapping, fracture microparameter measurement, and finite element method calculations were all taken into account. This work showed that more martensite transformation occurs with increasing Ni and that the ductile-brittle transition temperature drops with added Ni. The boundaries with low-angle misorientation (i.e., less than 4°) enhance microhardness and the optimal inclusion size is in the range of 0.3–0.45 μm, thus revealing the reasons for the resulting microhardness and toughness variations. In addition, the high-angle grain boundaries in close-packed plane packets effectually prevent cracks. From a practical perspective, 4 wt.% Ni could be the best design for welding wires in the present work. This work confirmed many of the results found in recently published literature. These results provide a reference for the composition design of welding wires and the processing method of WMs

Effects of Boron Content on the Microstructure and Impact Toughness of 12Cr1MoVR Low-Alloy Heat-Resistant Steel Weld Metals

The impact toughness of low-Cr heat-resistant steel weld metal is an important problem to broaden the application of low-Cr heat-resistant steel. In this study, the microstructure and impact toughness of 12Cr1MoVR low-alloy heat-resistant steel weld metals with various boron contents (B1 = 0.0028%, B2 = 0.0054%, and B3 = 0.0079%) were investigated. The microstructures of all weld metals were composed of block ferrite, carbides, and inclusions. Results indicated that with increased B content, prior austenite grain sizes decreased, and minor microstructure changes could be found. With the increase in B content from 0.0028% to 0.0054% to 0.0079%, the ductile–brittle transition temperature of the weld metals decreased from 30 to 0 to −14 °C, the toughness of weld metal increased, and the hardness slightly decreased, all of which are directly related to the refinement of prior austenite grain size because of the addition of B content. However, on the top-shelf zone, such as at the testing temperature of 80 °C, ductile fracture dominates the fracture surface; with the increase in B content, the size and density of inclusions increased gradually, which led to the decrease of the impact toughness at 80 °C when the B content was 0.0079%

Mesoporous NiCo₂O₄ Nanoplates on Three-Dimensional Graphene Foam as an Efficient Electrocatalyst for the Oxygen Reduction Reaction

Catalysts for the oxygen reduction reaction (ORR) are highly important in fuel cells and metal–air batteries. Cheap ORR catalysts with ultrahigh electrochemical activity, selectivity, and stability are extremely desirable but still remain challenging. Herein, mesoporous NiCo₂O₄ nanoplate (NP) arrays on three-dimensional (3D) graphene foam are shown to be a highly economical ORR catalyst. This mesoporous mixed-valence oxide can provide more electrocatalytic active sites with increased accessible surface area. In addition, graphene-foam-supported NiCo₂O₄ NP arrays have a 3D hierarchical porous structure, which is of great benefit to ion diffusion and electron transfer. As a result, the mesoporous NiCo₂O₄ NP arrays/graphene foam catalyst exhibits outstanding ORR performance with the four-electron reduction of O₂ to H₂O in alkaline media. Furthermore, the mesoporous catalyst shows enhanced electrocatalytic activity with a half-wave potential of 0.86 V vs RHE and better stability compared with a commercial Pt/C catalyst

Deep Learning versus Professional Healthcare Equipment: A Fine-Grained Breathing Rate Monitoring Model

In mHealth field, accurate breathing rate monitoring technique has benefited a broad array of healthcare-related applications. Many approaches try to use smartphone or wearable device with fine-grained monitoring algorithm to accomplish the task, which can only be done by professional medical equipment before. However, such schemes usually result in bad performance in comparison to professional medical equipment. In this paper, we propose DeepFilter, a deep learning-based fine-grained breathing rate monitoring algorithm that works on smartphone and achieves professional-level accuracy. DeepFilter is a bidirectional recurrent neural network (RNN) stacked with convolutional layers and speeded up by batch normalization. Moreover, we collect 16.17 GB breathing sound recording data of 248 hours from 109 and another 10 volunteers to train and test our model, respectively. The results show a reasonably good accuracy of breathing rate monitoring

Mesoporous and ultrathin arrays of cobalt nitride nanosheets for electrocatalytic oxygen evolution

Efficient electrocatalysts for oxygen evolution are vitally important for regenerative fuel cells and metal-air batteries. Herein, 3D mesoporous and ultrathin array of CoN nanosheets are synthesized on the Ni foam via thermal transformation of Co3O4 nanowire arrays. This array imparts enhanced active sites, mass diffusion, and electron transfer towards oxygen evolution reaction. The low overpotential of 323 mV at 30 mA cm−2, a Tafel slope of 74 mV dec−1, and a high potential conservation in a long process of electrolysis process are achieved. It is thus one robust and efficient electrocatalyst for OER. Keywords: CoN nanosheet, Oxygen evolution reaction, Electrocatalys