Heterogeneous Pairs Research Articles

High-Density Dual Atoms Pairs Coupling for Efficient Electromagnetic Wave Absorbers.

Dual atoms (DAs), characterized by flexible structural tunability and high atomic utilization, hold significant promise for atom-level coordination engineering. However, the rational design with high-density heterogeneous DAs pairs to promote electromagnetic wave (EMW) absorption performance remains a challenge. In this study, high-density Ni─Cu pairs coupled DAs absorbers are precisely constructed on a nitrogen-rich carbon substrate, achieving an impressive metal loading amount of 4.74wt.%, enabling a huge enhancement of the effective absorption bandwidth (EAB) of EMW from 0 to 7.8GHz. Furthermore, the minimum reflection loss (RLmin) is -70.96dB at a matching thickness of 3.60mm, corresponding to an absorption of >99.99% of the incident energy. Both experimental results and theoretical calculations indicate that the synergistic effect of coupled Ni─Cu pairs DAs sites results in the transfer of electron-rich sites from the initial N sites to the Cu sites, which induces a strong asymmetric polarization loss by this redistribution of local charge and significantly improves the EMW absorption performance. This work not only provides a strategy for the preparation of high-density DA pairs but also demonstrates the role of coupled DA pairs in precisely tuning coordination symmetry at the atomic level.

Interdependence of heterogeneous blockholders: Evidence from China

The co-holding of interdependent pairs of heterogeneous blocks can provide firms with stable financial support. The interdependence of heterogeneous blockholders’ investment decision has become an important frontier in the financial literature on large shareholders. In this paper, we study the interdependence of heterogeneous blocks in China. We find significant positive interdependence among blockholders of the same type. In heterogeneous block pairs, the financial–private pair shows positive interdependence. The findings are in contrast to those observed in the US. Under the mixed-ownership reform in China, our findings suggest the potential for cooperation among multiple blocks of the same type rather than between heterogeneous strategic partners.

Construction of heterogeneous frustrated lewis pairs based on covalent organic frameworks stabilized boron cations and investigation of cycloaddition reaction performance

Frustrated Lewis Pairs (FLPs) catalysts have been widely designed and synthesized in current chemistry, with the key being the selection of boron-based Lewis acids. Among novel boron-based Lewis acids, boron cations with higher Lewis acidity caused by cationic charge have been proven to have special reactivity. However, the current boron cations have only been used to construct homogeneous FLPs, which would be difficult to meet the industrial process demand for FLPs catalysts, possibly due to the lack of suitable stabilizers. In this paper, we propose a strategy to construct heterogeneous FLPs using covalent organic frameworks (COFs) stabilized boron cations as Lewis acid. Firstly, heterogeneous Lewis acid COFs@[B(C6F5)2]+[Al2Cl7]- was prepared by stabilizing boron cations with CN bonds in COFs, followed by introducing tBu3P to construct heterogeneous FLPs COFs@[B(C6F5)2]+[Al2Cl7]-/tBu3P. Meanwhile, these FLPs catalysts exhibited excellent catalytic and cyclic performance in the preparation of cyclic carbonates by CO2 addition from epoxy compounds.

A visible-infrared person re-identification method based on meta-graph isomerization aggregation module

Due to different imaging principles of visible-infrared cameras, there are modal differences between similar person images. For visible-infrared person re-identification (VI-ReID), existing works focus on extracting and aligning cross-modal global descriptors in the shared feature space, while ignoring local variations and graph-structural correlations in cross-modal image pairs. In order to bridge the modal differences with graph-structure between key-parts, a Meta-Graph Isomerization Aggregation Module (MIAM) is proposed, which includes Meta-Graph Node Isomerization Module (MNI) and Dual Aggregation Module (DA). In order to completely describe discriminative in-graph local features, MNI establishes meta-secondary cyclic isomorphism relations of in-graph local features by using a multi-branch embedding generation mechanism. Then, the local features not only contain the limited information of the fixed regions, but also benefit from the neighboring regions. Meanwhile, the secondary node generation process considers similar and different nodes of pedestrian graph structure to reduce the interference of identity differences. In addition, the Dual Aggregation (DA) module combines spatial self-attention and channel self-attention to establish the interdependence of modal heterogeneous graph structure pairs and achieve inter-modal feature aggregation. To match heterogeneous graph structures, a Node Center Joint Mining Loss (NCJM Loss) is proposed to constrain the distance between the node centers of heterogeneous graphs. The experiments performed on the SYSU-MM01, RegDB and LLCM public datasets demonstrate that the proposed method performs excellently in VI-ReID.

Retraction of: Direct Joining of a Heterogeneous Pair of Supramolecular Nanotubes and Reaction Control of a Guest Compound by Transportation in the Nanochannels

Retraction of: Direct Joining of a Heterogeneous Pair of Supramolecular Nanotubes and Reaction Control of a Guest Compound by Transportation in the Nanochannels

Network-based analysis of heterogeneous patient-matched brain and extracranial melanoma metastasis pairs reveals three homogeneous subgroups

Melanoma, the deadliest form of skin cancer, can metastasize to different organs. Molecular differences between brain and extracranial melanoma metastases are poorly understood. Here, promoter methylation and gene expression of 11 heterogeneous patient-matched pairs of brain and extracranial metastases were analyzed using melanoma-specific gene regulatory networks learned from public transcriptome and methylome data followed by network-based impact propagation of patient-specific alterations. This innovative data analysis strategy allowed to predict potential impacts of patient-specific driver candidate genes on other genes and pathways. The patient-matched metastasis pairs clustered into three robust subgroups with specific downstream targets with known roles in cancer, including melanoma (SG1: RBM38, BCL11B, SG2: GATA3, FES, SG3: SLAMF6, PYCARD). Patient subgroups and ranking of target gene candidates were confirmed in a validation cohort. Summarizing, computational network-based impact analyses of heterogeneous metastasis pairs predicted individual regulatory differences in melanoma brain metastases, cumulating into three consistent subgroups with specific downstream target genes.

A new method for handling heterogeneous data in bioinformatics

Heterogeneous data, especially a mixture of numerical and categorical data, widely exist in bioinformatics. Most of works focus on defining new distance metrics rather than learning discriminative metrics for mixed data. Here, we create a new support vector heterogeneous metric learning framework for mixed data. A heterogeneous sample pair kernel is defined for mixed data and metric learning is then converted to a sample pair classification problem. The suggested approach lends itself well to effective resolution through conventional support vector machine solvers. Empirical assessments conducted on mixed data benchmarks and cancer datasets affirm the exceptional efficacy demonstrated by the proposed modeling technique.

Printability disparities in heterogeneous material combinations via laser directed energy deposition: a comparative study

Additive manufacturing provides achievability for the fabrication of bimetallic and multi-material structures; however, the material compatibility and bondability directly affect the parts’ formability and final quality. It is essential to understand the underlying printability of different material combinations based on an adapted process. Here, the printability disparities of two common and attractive material combinations (nickel- and iron-based alloys) are evaluated at the macro and micro levels via laser directed energy deposition (DED). The deposition processes were captured using in situ high-speed imaging, and the dissimilarities in melt pool features and track morphology were quantitatively investigated within specific process windows. Moreover, the microstructure diversity of the tracks and blocks processed with varied material pairs was comparatively elaborated and, complemented with the informative multi-physics modeling, the presented non-uniformity in mechanical properties (microhardness) among the heterogeneous material pairs was rationalized. The differences in melt flow induced by the unlike thermophysical properties of the material pairs and the resulting element intermixing and localized re-alloying during solidification dominate the presented dissimilarity in printability among the material combinations. This work provides an in-depth understanding of the phenomenological differences in the deposition of dissimilar materials and aims to guide more reliable DED forming of bimetallic parts.

Algorithm for Eliminating Mismatched Feature Points in Heterogeneous Images Pairs Under Spatial Constraints

Algorithm for Eliminating Mismatched Feature Points in Heterogeneous Images Pairs Under Spatial Constraints

Numerical investigation of heterogeneous soft particle pairs in inertial microfluidics.

The formation of pairs of particles or cells of different types in microfluidic channels can be desired or detrimental in healthcare applications. It is still unclear what role softness heterogeneity plays in the formation of these particle pairs. We use an in-house lattice-Boltzmann-immersed-boundary-finite-element solver to simulate a pair of particles with different softness flowing through a straight channel with a rectangular cross-section under initial conditions representative of a dilute suspension. We find that softness heterogeneity significantly affects the pair dynamics, determining whether a pair will form or not, and determining the lateral and inter-particle equilibrium behaviour in the pair. We also observe close matches between the transient deformation of particles in a linear pair and single particles in isolation. These results further our understanding of pair behaviour, providing a foundation for understanding particle train formation, and open up the potential to develop reduced-order models for particle pair formation based upon the behaviour of single particles.

Alignment of density maps in Wasserstein distance.

In this article, we propose an algorithm for aligning three-dimensional objects when represented as density maps, motivated by applications in cryogenic electron microscopy. The algorithm is based on minimizing the 1-Wasserstein distance between the density maps after a rigid transformation. The induced loss function enjoys a more benign landscape than its Euclidean counterpart and Bayesian optimization is employed for computation. Numerical experiments show improved accuracy and efficiency over existing algorithms on the alignment of real protein molecules. In the context of aligning heterogeneous pairs, we illustrate a potential need for new distance functions.

Iterative Network for Disparity Prediction with Infrared and Visible Light Images Based on Common Features.

In recent years, the range of applications that utilize multiband imaging has significantly expanded. However, it is difficult to utilize multichannel heterogeneous images to achieve a spectral complementarity advantage and obtain accurate depth prediction based on traditional systems. In this study, we investigate CFNet, an iterative prediction network, for disparity prediction with infrared and visible light images based on common features. CFNet consists of several components, including a common feature extraction subnetwork, context subnetwork, multimodal information acquisition subnetwork, and a cascaded convolutional gated recurrent subnetwork. It leverages the advantages of dual-band (infrared and visible light) imaging, considering semantic information, geometric structure, and local matching details within images to predict the disparity between heterogeneous image pairs accurately. CFNet demonstrates superior performance in recognized evaluation metrics and visual image observations when compared with other publicly available networks, offering an effective technical approach for practical heterogeneous image disparity prediction.

Micromixing optimization of non-newtonian fluids with heterogeneous zeta potential

This paper aims to improve micromixing in T-shaped microchannels using heterogeneous zeta potential, which is essential for analyzing biochemical fluids in various industrial applications. We investigate the impact of different parameters, including zeta potential, applied electric field, and a number of heterogeneous surface patterns, on microchannel mixing performance. Our results demonstrate that increasing the zeta potential on the heterogeneous pair patches amplifies the recirculation zone, improving mixing performance within the microchannel. Therefore, we recommend increasing the value of heterogeneous zeta potential on the patches to enhance the mixing performance further. Additionally, we examine the non-Newtonian behavior of the fluid using the power law model and find that pseudoplastic fluids (n < 1) have lower mixing performance compared to dilatant fluids (n > 1) due to higher velocity. We evaluate the effects of various parameters on mixing performance to design an efficient micromixer that achieves rapid and homogeneous mixing of sample fluids. Finally, we optimize the micromixing efficiency using Taguchi to achieve the optimum value of mixing. The optimized value of micromixing efficiency for the corresponding non-Newtonian fluid is also determined.

Effect of IGF-1 C472T, GH C2141G, and GHR T914A polymorphisms on growth performance and feed efficiency in young Kazakh white-headed cattle.

Improving the feed efficiency of beef cattle is necessary to increase the profitability of meat production. Implementing marker-assisted selection breeding systems can improve the genetic potential of beef cattle for increased productivity. This research aimed to study the effects of insulin-like growth factor (IGF)-1 C472T, growth hormone (GH) C2141G, and GH receptor (GHR) T914A polymorphisms on growth performance and feed efficiency in young Kazakh white-headed cattle. Young Kazakh white-headed cattle (n = 50) were grouped after weaning according to sex (28 bulls and 22 heifers) and they were genotyped according to the IGF-1 C472T, GH C2141G, and GHR T914A polymorphisms. The test period was conducted from 8 to 15 months of age. The experimental animals were evaluated for live weight (LW) at the beginning and end of the test period. They were also assessed for average daily gain, hip height, metabolic mid-weight (MMWT), actual dry matter intake (DMI), and residual feed intake (RFI). Significant differences in MMWT were found between the bulls with the IGF-1TT and IGF-1CT genotypes, which was a 2.2 kg increase in heterozygous cattle (p < 0.05). Heterozygous IGF-1CT bulls differed with a higher DMI of 0.087 kg/day (p < 0.05) compared to IGF-1TT homozygotes. Carriers of the IGF-1TT genotype had the greatest feed efficiency at 0.068 kg/day (p < 0.05). Heifers with the GHCC genotype differed in their maximum DMI with an increase of 1.17%-1.57% (p < 0.05) relative to the other genotypes. The G allele in the GH C2141G polymorphism was associated with better (p < 0.05) feed efficiency in the Kazakh white-headed breed. The minimum DMI and RFI in GHR T914A heterozygous heifers were significantly inferior (p < 0.05) to the other genotypes. Association studies of the IGF-1 C472T, GH C2141G, and GHR T914A polymorphisms indicate a relationship between growth, development, and feed efficiency with the genetic characteristics of young Kazakh white-headed cattle. A significant (p < 0.05) dominant effect was found in the IGF-1 gene in bulls and in the GHR gene in heifers, which should be considered when breeding with heterogeneous parental pairs. The negative effect of the allele substitution in the IGF-1 C472T polymorphism was observed in the LW of heifers (-3.25 kg) at the age of 8 months and bulls (-6.05 kg) at 15 months. The substitution in the GH C2141G polymorphism was associated with a significant reduction in DMI by 0.036 kg (p < 0.05) and an increase in feed efficiency by 0.023 kg (p < 0.05) during the rearing of heifers. These results can improve the production efficiency of mature herds of Kazakh white-headed cattle.

Rethinking Asian values in journalism: the case of the pressroom in the South Korean presidential office

ABSTRACT The present study explored how Asian values are reproduced in South Korean journalistic practices. In-depth interviews were conducted with journalists covering the Korean presidential office. The results revealed that Asian values prioritizing harmony, authority, national interests, and order influence the pressroom of the presidential office. The reporters run dozens of mobile-messenger-based groups, called kumis, with fellow reporters to share information and jointly contact officials. This practice embodies an Asian value – in-group harmony, derived from Confucian communalism. The beat (areas covered by reporters) and pressroom system institutionalizes Asian values by allowing only registered reporters to access the presidential office. The president’s staff abuses off-the-records and embargoes, but the reporters accept them out of respect for the president’s authority. The reporters often make headlines on the president’s national security agenda. We also found the hybridity of Asian values and watchdogs in the Korean reporters’ practices. Partisan bias is a mediator determining whether to activate Asian values or watchdog roles. Asian values are in excess between ideologically homogenous reporters and presidents, while government watchdogs are stimulated between heterogeneous pairs. As press freedom grows in Asia, the model of blending Asian values with other journalistic attributes is likely to be found often.

Q-neighbor Ising model on multiplex networks with partial overlap of nodes.

The q-neighbor Ising model for the opinion formation on multiplex networks with two layers in the form of random graphs (duplex networks), the partial overlap of nodes, and LOCAL&AND spin update rule was investigated by means of the pair approximation and approximate master equationsas well as Monte Carlo simulations. Both analytic and numerical results show that for different fixed sizes of the q-neighborhood and finite mean degrees of nodes within the layers the model exhibits qualitatively similar critical behavior as the analogous model on multiplex networks with layers in the form of complete graphs. However, as the mean degree of nodes is decreased the discontinuous ferromagnetic transition, the tricritical point separating it from the continuous transition, and the possible coexistence of the paramagnetic and ferromagnetic phases at zero temperature occur for smaller relative sizes of the overlap. Predictions of the simple homogeneous pair approximation concerning the critical behavior of the model under study show good qualitative agreement with numerical results; predictions based on the approximate master equationsare usually quantitatively more accurate but yet not exact. Two versions of the heterogeneous pair approximation are also derived for the model under study, which, surprisingly, yield predictions only marginally different or even identical to those of the simple homogeneous pair approximation. In general, predictions of all approximations show better agreement with the results of Monte Carlo simulations in the case of continuous than discontinuous ferromagnetic transition.

Construction of Boron‐ and Nitrogen‐Enriched Nanoporous π‐Conjugated Networks Towards Enhanced Hydrogen Activation

AbstractBoron‐enriched scaffolds have demonstrated unique features and promising performance in the field of catalysis towards the activation of small gas molecules. However, there is still a lack of facile approaches capable of achieving high B doping and abundant porous channels in the targeted catalysts. Herein, construction of boron‐ and nitrogen‐enriched nanoporous π‐conjugated networks (BN‐NCNs) was achieved via a facile ionothermal polymerization procedure with hexaazatriphenylenehexacarbonitrile [HAT(CN)6] sodium borohydride as the starting materials. The as‐produced BN‐NCN scaffolds were featured by high heteroatoms doping (B up to 23 wt. % and N: up to 17 wt. %) and permanent porosity (surface area up to 759 m2 g−1 mainly contributed by micropores). With the unsaturated bonded B species acting as the active Lewis acid sites and defected N species acting as the active Lewis base sites, those BN‐NCNs delivered attractive catalytic performance towards H2 activation/dissociation in both gaseous and liquid phase, acting as efficient metal‐free heterogeneous frustrated Lewis pairs (FLPs) catalysts in hydrogenation procedures.

Pareto optimization with small data by learning across common objective spaces

In multi-objective optimization, it becomes prohibitively difficult to cover the Pareto front (PF) as the number of points scales exponentially with the dimensionality of the objective space. The challenge is exacerbated in expensive optimization domains where evaluation data is at a premium. To overcome insufficient representations of PFs, Pareto estimation (PE) invokes inverse machine learning to map preferred but unexplored regions along the front to the Pareto set in decision space. However, the accuracy of the inverse model depends on the training data, which is inherently scarce/small given high-dimensional/expensive objectives. To alleviate this small data challenge, this paper marks a first study on multi-source inverse transfer learning for PE. A method to maximally utilize experiential source tasks to augment PE in the target optimization task is proposed. Information transfers between heterogeneous source-target pairs is uniquely enabled in the inverse setting through the unification provided by common objective spaces. Our approach is tested experimentally on benchmark functions as well as on high-fidelity, multidisciplinary simulation data of composite materials manufacturing processes, revealing significant gains to the predictive accuracy and PF approximation capacity of Pareto set learning. With such accurate inverse models made feasible, a future of on-demand human-machine interaction facilitating multi-objective decisions is envisioned.

Dual S-scheme graphitic carbon-doped α-Bi2O3/β-Bi2O3/Bi5O7I ternary heterojunction photocatalyst for the degradation of Bisphenol A

Graphitic carbon-doped α-Bi2O3/β-Bi2O3/Bi5O7I ternary heterojunction photocatalysts were synthesized by calcination of urea, malonic acid and BiOIO3 in a simple and low-cost way. The photocatalytic activity of the photocatalysts was evaluated by the degradation of Bisphenol A (BPA) under simulated sunlight irradiation. The reaction rate constants of graphitic carbon-doped α-Bi2O3/β-Bi2O3/Bi5O7I were 4.6, 4.2, 17.8, and 231 times higher than those of BIO-550 (Bi5O7I), MA-BIO (α-Bi2O3/Bi5O7I), MACN and CN-BIO (α-Bi2O3/β-Bi2O3), respectively. The enhanced photocatalytic activity is due to the high separation efficiency of heterogeneous photogenerated electron-hole pairs, which is caused by the synergistic effect of the heterojunction between the different components and the efficient electron transfer efficiency of graphitic carbon. In addition, a dual S-scheme heterojunction mechanism has been proposed to explain the excellent redox properties of the composites.

Embedding knowledge graph of patent metadata to measure knowledge proximity

AbstractKnowledge proximity refers to the strength of association between any two entities in a structural form that embodies certain aspects of a knowledge base. In this work, we operationalize knowledge proximity within the context of the US Patent Database (knowledge base) using a knowledge graph (structural form) named “PatNet” built using patent metadata, including citations, inventors, assignees, and domain classifications. We train various graph embedding models using PatNet to obtain the embeddings of entities and relations. The cosine similarity between the corresponding (or transformed) embeddings of entities denotes the knowledge proximity between these. We compare the embedding models in terms of their performances in predicting target entities and explaining domain expansion profiles of inventors and assignees. We then apply the embeddings of the best‐preferred model to associate homogeneous (e.g., patent–patent) and heterogeneous (e.g., inventor–assignee) pairs of entities.