Collaborative Distribution Alignment for 2D image-based 3D shape retrieval

Retrieving 3D shapes with 2D images has become a popular research area nowadays, and a great deal of work has been devoted to reducing the discrepancy between 3D shapes and 2D images to improve retrieval performance. However, most approaches ignore the semantic information and decision boundaries of the two domains, and cannot achieve both domain alignment and category alignment in one module. In this paper, a novel Collaborative Distribution Alignment (CDA) model is developed to address the above existing challenges. Specifically, we first adopt a dual-stream CNN, following a similarity guided constraint module, to generate discriminative embeddings for input 2D images and 3D shapes (described as multiple views). Subsequently, we explicitly introduce a joint domain-class alignment module to dynamically learn a class-discriminative and domain-agnostic feature space, which can narrow the distance between 2D image and 3D shape instances of the same underlying category, while pushing apart the instances from different categories. Furthermore, we apply a decision boundary refinement module to avoid generating class-ambiguity embeddings by dynamically adjusting inconsistencies between two discriminators. Extensive experiments and evaluations on two challenging benchmarks, MI3DOR and MI3DOR-2, demonstrate the superiority of the proposed CDA method for 2D image-based 3D shape retrieval task.     

成型温度对PBX装药内部质量及性能的影响

针对压制成型的PBX炸药装药，选择CT无损检测、巴西实验和扫描电镜检测等技术，对比研究了室温和加热两种温度下压制成型的炸药装药内部质量、静态力学性能和细观破坏形式。结果表明，加热压制有利于改善炸药装药的内部质量，可避免产生初始损伤，且提高了装药的力学性能。细观尺度上，室温压制成型的装药主要发生界面脱黏破坏，加热压制成型装药的主要破坏形式是穿晶断裂。     

复杂环境下氧化铝储槽定向爆破拆除

在较为复杂的环境下,爆破拆除钢筋混凝土氧化铝储槽。该储槽自重大、呈圆形,内有4根立柱支撑下料漏斗。为使储槽顺利定向倒塌,通过爆破方案选择、参数确定,采取梯形切口和预处理以及安全防护和减振措施,使储槽爆破拆除获圆满成功。     

Enhancing the dehydrogenation properties of LiAlH4 using K2NiF6 as additive

Lithium alanate (LiAlH₄) is a material that can be potentially used for solid-state hydrogen storage due to its high hydrogen content (10.5 wt%). Nevertheless, a high desorption temperature, slow desorption kinetic, and irreversibility have restricted the application of LiAlH₄ as a solid-state hydrogen storage material. Hence, to lower the decomposition temperature and to boost the dehydrogenation kinetic, in this study, we applied K₂NiF₆ as an additive to LiAlH₄. The addition of K₂NiF₆ showed an excellent improvement of the LiAlH₄ dehydrogenation properties. After adding 10 wt% K₂NiF₆, the initial decomposition temperature of LiAlH₄ within the first two dehydrogenation steps was lowered to 90 °C and 156 °C, respectively, that is 50 °C and 27 °C lower than that of the аs-milled LiAlH₄. In terms of dehydrogenation kinetics, the dehydrogenation rate of K₂NiF₆-doped LiAlH₄ sample was significantly higher as compared to аs-milled LiAlH₄. The K₂NiF₆-doped LiAlH₄ sample can release 3.07 wt% hydrogen within 90 min, while the milled LiAlH₄ merely release 0.19 wt% hydrogen during the same period. According to the Arrhenius plot, the apparent activation energies for the desorption process of K₂NiF₆-doped LiAlH₄ are 75.0 kJ/mol for the first stage and 88.0 kJ/mol for the second stage. These activation energies are lower compared to the undoped LiAlH₄. The morphology study showed that the LiAlH₄ particles become smaller and less agglomerated when K₂NiF₆ is added. The in situ formation of new phases of AlNi and LiF during the dehydrogenation process, as well as a reduction in particle size, is believed to be essential contributors in improving the LiAlH₄ dehydrogenation characteristics.     

Decomposition and replacement: Spatial knowledge distillation for monocular depth estimation

Knowledge distillation has become a key technique for making smart and light-weight networks through model compression and transfer learning. Unlike previous methods that applied knowledge distillation to the classification task, we propose to exploit the decomposition-and-replacement based distillation scheme for depth estimation from a single RGB color image. To do this, Laplacian pyramid-based knowledge distillation is firstly presented in this paper. The key idea of the proposed method is to transfer the rich knowledge of the scene depth, which is well encoded through the teacher network, to the student network in a structured way by decomposing it into the global context and local details. This is fairly desirable for the student network to restore the depth layout more accurately with limited resources. Moreover, we also propose a new guidance concept for knowledge distillation, so-called ReplaceBlock, which replaces blocks randomly selected in the decoded feature of the student network with those of the teacher network. Our ReplaceBlock gives a smoothing effect in learning the feature distribution of the teacher network by considering the spatial contiguity in the feature space. This process is also helpful to clearly restore the depth layout without the significant computational cost. Based on various experimental results on benchmark datasets, the effectiveness of our distillation scheme for monocular depth estimation is demonstrated in details. The code and model are publicly available at : https://github.com/tjqansthd/Lap_Rep_KD_Depth.     

Investigation of liquid water heterogeneities in large area proton exchange membrane fuel cells using a Darcy two-phase flow model in a multiphysics code

Proper management of the liquid water and heat produced in proton exchange membrane (PEM) fuel cells remains crucial to increase both its performance and durability. In this study, a two-phase flow and multicomponent model, called two-fluid model, is developed in the commercial COMSOL Multiphysics® software to investigate the liquid water heterogeneities in large area PEM fuel cells, considering the real flow fields in the bipolar plate. A macroscopic pseudo-3D multi-layers approach has been chosen and generalized Darcy's relation is used both in the membrane-electrode assembly (MEA) and in the channel. The model considers two-phase flow and gas convection and diffusion coupled with electrochemistry and water transport through the membrane. The numerical results are compared to one-fluid model results and liquid water measurements obtained by neutron imaging for several operating conditions. Finally, according to the good agreement between the two-fluid and experimentation results, the numerical water distribution is examined in each component of the cell, exhibiting very heterogeneous water thickness over the cell surface.     

Impedance matching optimization of SiCf/Si3N4–SiOC composites for excellent microwave absorption properties

SiC fiber reinforced ceramic matrix composites (SiC_f-CMCs) are considered to be one of the most promising materials in the electromagnetic (EM) stealth of aero-engines, which is expected to achieve strong absorption and broad-band performance. Multiscale structural design was applied to SiC_f/Si₃N₄–SiOC composites by construction of micro/nanoscale heterogeneous interfaces and macro double-layer impedance matching structure. SiC_f/Si₃N₄–SiOC composites were fabricated by using SiC fibers with different conductivities and SiOC–Si₃N₄ matrices with gradient impedance structures to improve impedance matching effectively. Owing to its unique structure, SiC_f/Si₃N₄–SiOC composites (A3-composites) achieved excellent EM wave absorption performance with a minimum reflection coefficient (RC_min) of ?25.1 dB at 2.45 mm and an effective absorption bandwidth (EAB) of 4.0 GHz at 2.85 mm in X-band. Moreover, double-layer SiC_f/Si₃N₄–SiOC with an improved impedance matching structure obtained an RC_min of ?56.9 dB and an EAB of 4.2 GHz at 3.00 mm, which means it can absorb more than 90% of the EM waves in the whole X-band. The RC is less than ?8 dB at 2.6–2.8 mm from RT to 600 °C in the whole X-band, displaying excellent high-temperature absorption performance. The results provide a new design opinion for broad-band EM absorbing SiC_f-CMCs at high temperatures.     

A novel combustion fuel for the synthesis of nanocrystalline ZnAl2O4 particles based on the thermodynamic correlations and their structural and optical studies

ZnAl₂O₄ nanocrystalline particles were prepared using the solution combustion method using a new combustion fuel, Leucine. The prepared samples' structural, microstructural–elemental composition, and optical characteristics were investigated using XRD, SEM-EDS, and UV–Visible spectroscopy. As-synthesized ZnAl₂O₄ nanoparticles are polycrystalline, with no secondary phases, and crystallized in a cubic - spinel structure. The polycrystalline nature of the prepared sample is due to the exothermicity of fuel and oxidizer, which demonstrate that the fuel utilized (Leucine) provided adequate energy for the production of nanoparticles in their as-synthesized form, as supported by adiabatic temperature through thermodynamic calculations. The thermodynamic calculations also include a universal method to estimate the specific heat capacity at constant pressure. Furthermore, even after 2 h of calcination at 600 °C, ZnAl₂O₄ exhibits a single phase with no secondary phases, indicating the material stability and single-phase nature. The crystallinity of ZnAl₂O₄ nanoparticles was observed to increase with increasing annealing temperature. SEM micrographs of as-synthesized samples exhibit the formation of dense particles, voids, and pores in the as-synthesized sample. In addition, tiny aggregates were detected on the surface of more prominent clusters, which reduced as the calcination progressed. In addition, calcined samples exhibit a greater optical reflectance than as-synthesized samples. Tauc's graphs were used to compute the optical energy bandgap. The calculated energy band gap is redshifted to that of the bulk material. The bandgap energy decreases upon calcination, suggesting that the prepared materials have a larger crystallite size or more crystallinity. Correlations were found between the T_ad, and the structural and optical properties of the prepared samples. The findings suggest that Leucine could be used as a novel combustion fuel to produce crystalline ZnAl₂O₄ nanoparticles in their as-synthesis form.     

具有保护肠上皮细胞HT-29免受大肠杆菌和H2O2损伤潜力的植物乳杆菌的筛选

益生菌可在肠道定植从而发挥抗炎或抗氧化活性，有利于宿主肠道健康。本实验研究了从新疆传统发酵乳制品中分离得到的8?株植物乳杆菌对大肠杆菌侵袭和过氧化氢刺激肠上皮细胞HT-29的保护作用。结果表明：在8?株植物乳杆菌中，植物乳杆菌35具有最高的黏附能力。植物乳杆菌35可通过取代、竞争、排阻的方式抑制大肠杆菌对HT-29细胞的黏附，抑制率分别为42.60%、59.17%、60.19%。植物乳杆菌35及其多糖可抑制大肠杆菌刺激HT-29细胞产生白细胞介素-8；同时保护HT-29细胞免受过氧化氢的损伤，增加超氧化物歧化酶、谷胱甘肽过氧化物酶活力水平并降低丙二醛含量。结论：植物乳杆菌35及其粗胞外多糖具有抑制大肠杆菌O157诱导的炎症性肠病的潜力。     

Topological homogenization of metamaterial variability

With the proliferation of additive manufacturing and 3D printing technologies, a broader palette of material properties can be elicited from cellular solids, also known as metamaterials, architected foams, programmable materials, or lattice structures. Metamaterials are designed and optimized under the assumption of perfect geometry and a homogeneous underlying base material. Yet in practice real lattices contain thousands or even millions of complex features, each with imperfections in shape and material constituency. While the role of these defects on the mean properties of metamaterials has been well studied, little attention has been paid to the stochastic properties of metamaterials, a crucial next step for high reliability aerospace or biomedical applications. In this work we show that it is precisely the large quantity of features that serves to homogenize the heterogeneities of the individual features, thereby reducing the variability of the collective structure and achieving effective properties that can be even more consistent than the monolithic base material. In this first statistical study of additive lattice variability, a total of 239 strut-based lattices were mechanically tested for two pedagogical lattice topologies (body centered cubic and face centered cubic) at three different relative densities. The variability in yield strength and modulus was observed to exponentially decrease with feature count (to the power −0.5), a scaling trend that we show can be predicted using an analytic model or a finite element beam model. The latter provides an efficient pathway to extend the current concepts to arbitrary/complex geometries and loading scenarios. These results not only illustrate the homogenizing benefit of lattices, but also provide governing design principles that can be used to mitigate manufacturing inconsistencies via topological design.