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.     

高深溜井井筒堵塞的爆破处理实践

溜井是采用平硐-溜井方式开拓矿山的运输咽喉,溜井正常与否对矿山生产影响极大。本文通过黑沟铁矿高深溜井井筒堵塞处理实例,对其堵塞爆破处理方法及经验做了系统分析。重点介绍的爆破冲击波破拱疏通高深溜井井筒高位堵塞的爆破方法,富有新意,可供国内外同类型矿山参考借鉴。     

Presentation attack detection based on two-stream vision transformers with self-attention fusion

Aiming at the performance degradation of the existing presentation attack detection methods due to the illumination variation, a two-stream vision transformers framework (TSViT) based on transfer learning in two complementary spaces is proposed in this paper. The face images of RGB color space and multi-scale retinex with color restoration (MSRCR) space are fed to TSViT to learn the distinguishing features of presentation attack detection. To effectively fuse features from two sources (RGB color space images and MSRCR images), a feature fusion method based on self-attention is built, which can effectively capture the complementarity of two features. Experiments and analysis on Oulu-NPU, CASIA-MFSD, and Replay-Attack databases show that it outperforms most existing methods in intra-database testing and achieves good generalization performance in cross-database testing.     

DFD-SS: Document Forgery Detection using Spectral – Spatial Features for Hyperspectral Images

In the present era of machines and edge-cutting technologies, still document frauds persist. They are done intuitively by using almost identical inks, that it becomes challenging to detect them—this demands an approach that efficiently investigates the document and leaves it intact. Hyperspectral imaging is one such a type of approach that captures the images from hundreds to thousands of spectral bands and analyzes the images through their spectral and spatial features, which is not possible by conventional imaging. Deep learning is an edge-cutting technology known for solving critical problems in various domains. Utilizing supervised learning imposes constraints on its usage in real scenarios, as the inks used in forgery are not known prior. Therefore, it is beneficial to use unsupervised learning. An unsupervised feature extraction through a Convolutional Autoencoder (CAE) followed by Logistic Regression (LR) for classification is proposed (CAE-LR). Feature extraction is evolved around spectral bands, spatial patches, and spectral-spatial patches. We inspected the impact of spectral, spatial, and spectral-spatial features by mixing inks in equal and unequal proportion using CAE-LR on the UWA writing ink hyperspectral images dataset for blue and black inks. Hyperspectral images are captured at multiple correlated spectral bands, resulting in information redundancy handled by restoring certain principal components. The proposed approach is compared with eight state-of-art approaches used by the researchers. The results depicted that by using the combination of spectral and spatial patches, the classification accuracy enhanced by 4.85% for black inks and 0.13% for blue inks compared to state-of-art results. In the present scenario, the primary area concern is to identify and detect the almost similar inks used in document forgery, are efficiently managed by the proposed approach.     

CNT-interconnected iron-doped NiP2/Ni2P heterostructural nanoflowers as high-efficiency electrocatalyst for oxygen evolution reaction

Oxygen evolution reaction (OER) plays a decisive role in electrolytic water splitting. However, it is still challengeable to develop low-cost and efficient OER electrocatalysts. Herein, we present a combination strategy via heteroatom doping, hetero-interface engineering and introducing conductive skeleton to synthesize a hybrid OER catalyst of CNT-interconnected iron-doped NiP₂/Ni₂P (Fe-(NiP₂/Ni₂P)@CNT) heterostructural nanoflowers by a simple hydrothermal reaction and subsequent phosphorization process. The optimized Fe-(NiP₂/Ni₂P)@CNT catalyst delivers an ultralow Tafel slope of 46.1 mV dec^?1 and overpotential of 254 mV to obtain 10 mA cm^?2, which are even better than those of commercial OER catalyst RuO₂. The excellent OER performance is mainly attributed to its unique nanoarchitecture and the synergistic effects: the nanoflowers constructed by a 2D-like nanosheets guarantee large specific area and abundant active sites; the highly conductive CNT skeleton and the electronic modulation by the heterostructural NiP₂/Ni₂P interface and the hetero-atom doping can improve the catalytic activity; porous nanostructure benefits electrolyte penetration and gas release; most importantly, the rough surface and rich defects caused by phosphorization process can further enhance the OER performance. This work provides a deep insight to boost catalytic performance by heteroatom doping and interface engineering for water splitting.     

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.     

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.     

Metal chlorides-promoted ammonia absorption of deep eutectic solvent

Ammonia is considered as a promising hydrogen or energy carrier. Ammonia absorption or adsorption is an important aspect for both ammonia removal, storage and separation applications. To these ends, a wide range of solid and liquid sorbents have been investigated. Among these, the deep eutectic solvent (DES) is emerging as a promising class of ammonia absorbers. Herein, we report a novel type of DES, i.e., metal-containing DESs for ammonia absorption. Specifically, the NH₃ absorption capacity is enhanced by ca. 18.1–36.9% when a small amount of metal chlorides, such as MgCl₂, MnCl₂ etc., are added into a DES composed of resorcinol (Res) and ethylene glycol (EG). To our knowledge, the MgCl₂/Res/EG (0.1:1:2) DES outperforms most of the reported DESs. The excellent NH₃ absorption performances of metal–containing DESs have been attributed to the synergy of Lewis acid–base and hydrogen bonding interactions. Additionally, good reversibility and high NH₃/CO₂ selectivity are achieved over the MgCl₂/Res/EG (0.1:1:2) DES, which enables it to be a potential NH₃ absorber for further investigations.