Similar Local Structure Research Articles

Investigating the Bromoform Membrane Interactions Using Atomistic Simulations and Machine Learning: Implications for Climate Change Mitigation.

Methane emissions from livestock contribute to global warming. Seaweeds used as food additive offer a promising emission mitigation strategy because seaweeds are enriched in bromoform─a methanogenesis inhibitor. Therefore, understanding bromoform storage and production in seaweeds and particularly in a cell-like environment is crucial. As a first step toward this aim, we present an atomistic description of bromoform dynamics, diffusion, and aggregation in the presence of lipid membranes. Using all-atom molecular dynamics simulations with customized CHARMM-formatted bromoform force field files, we investigate the interactions of bromoform and lipid bilayer across various concentrations. Bromoform penetrates membranes and at high concentrations forms aggregates outside the membrane without affecting membrane thickness or lipid tail order. Aggregates outside the membrane influence the membrane curvature. Within the membrane, bromoform preferentially localizes in the membrane hydrophobic core and diffuses the slowest along the membrane normal. Employing general local-atomic descriptors and unsupervised machine learning, we demonstrate the similarity of bromoform local structures between the liquid and aggregated forms.

Discovery of narrow-band emitting phosphor Na5Al3F14:Eu2+ using local structure similarity

Narrow-band emitting phosphors are utilized to improve the color purity and expand the color gamut of display. Data-driven approach is an effective technique to search the host material of the narrow-band emitting phosphors. Herein, we discover a new narrow-band emitting phosphor Na5Al3F14:Eu2+ by a local structure similarity. 2D scatter plot of the structural similarity revealed that the local structure of Na1-site of the Na5Al3F14 located to near the Sr-sites of the narrow-band emitting phosphor SrLiAl3N4:Eu2+. A powder of the narrow-band emitting phosphor Na5Al3F14:Eu2+ was successfully synthesized by a solid-state reaction method. The Na5Al3F14:Eu2+ phosphor exhibited a narrow-band emission at 393 nm with a full width at half-maximum of 30 nm (1549 cm−1). The internal and external quantum efficiency was 56.0 % and 45.0 %, respectively. The photoluminescence intensity at 200°C was maintained at 63 % of room temperature. This approach accelerates the discovery of new phosphors with specific luminescence properties.

RNAtango: Analysing and comparing RNA 3D structures via torsional angles.

RNA molecules, essential for viruses and living organisms, derive their pivotal functions from intricate 3D structures. To understand these structures, one can analyze torsion and pseudo-torsion angles, which describe rotations around bonds, whether real or virtual, thus capturing the RNA conformational flexibility. Such an analysis has been made possible by RNAtango, a web server introduced in this paper, that provides a trigonometric perspective on RNA 3D structures, giving insights into the variability of examined models and their alignment with reference targets. RNAtango offers comprehensive tools for calculating torsion and pseudo-torsion angles, generating angle statistics, comparing RNA structures based on backbone torsions, and assessing local and global structural similarities using trigonometric functions and angle measures. The system operates in three scenarios: single model analysis, model-versus-target comparison, and model-versus-model comparison, with results output in text and graphical formats. Compatible with all modern web browsers, RNAtango is accessible freely along with the source code. It supports researchers in accurately assessing structural similarities, which contributes to the precision and efficiency of RNA modeling.

Achieving High‐Voltage Stability in Li‐Rich Ni‐Rich Oxides with Local W/Ni(Li) Superstructure

AbstractCreating high‐energy‐density cathodes is crucial for building next‐generation lithium‐ion batteries. However, materials exploration along two main directions, namely Li‐rich or Ni‐rich oxides, has encountered bottlenecks. To get rid of the impasse, here a “Li‐rich Ni‐rich” route is consolidated by designing a new family of Li1+yNi(3‐5y)/3W2y/3O2 oxides with high‐voltage cycling stability up to 4.5 V and high capacities over 230 mAh g−1. It is discovered that W6+ is largely incorporated into the LiNiO2 lattice, forming W/Ni(Li) inverse honeycomb‐ordered nano‐domains. These Li‐rich domains enable reversible anionic redox, clearly demonstrated by X‐ray absorption spectroscopy, resonant inelastic X‐ray scattering, transmission electron microscopy, and nuclear magnetic resonance, which is linked to improved electrochemical performance. Furthermore, the incorporation of W6+ into the lattice proves to be the key to generating electrochemically active Li‐rich domains irrespective of Li stoichiometry given that a similar local structure is found in W‐substituted non‐Li‐rich oxides. This therefore implies the underestimated role of high‐valence cations in tuning the structure and electrochemistry of Ni‐rich oxides. These results underline the necessity of a Li‐rich composition in the request for reversible high capacity, reinforcing the promise of a “Li‐rich Ni‐rich” avenue for developing advanced cathodes.

Different or the same? exploring the physicochemical properties and molecular mobility of celecoxib amorphous forms

The influence of different preparation methods on the physicochemical properties of amorphous solid forms have gained considerable attention, especially with recent publications on pharmaceutical polyamorphism. In the present study, we have investigated the possible occurrence of polyamorphism in the drug celecoxib (CEL) by investigating the thermal behavior, morphology, structure, molecular mobility and physical stability of amorphous CEL obtained by quench-cooling (QC), ball milling (BM) and spray drying (SD). Similar glass transition temperatures but different recrystallization behaviors were observed for CEL-QC, CEL-BM and CEL-SD using modulated differential scanning calorimetry analysis. A correlation between the different recrystallization behaviors of the three CEL amorphous forms and the respective distinct powder morphologies, was also found. Molecular dynamics simulations however, reveal that CEL presents similar molecular conformational distributions when subjected to QC and SD. Moreover, the obtained molecular conformational distributions of CEL are different from the ones found in its crystal structure and also from the ones found in the lowest-energy structure obtained by quantum mechanical calculations. The type and strength of CEL hydrogen bond interactions found in CEL-QC and CEL-SD systems are almost identical, though different from the ones presented in the crystal structure. Pair distribution function analyses and isothermal microcalorimetry show similar local structures and structural relaxation times, respectively, for CEL-QC, CEL-BM and CEL-SD. The present work shows that not only similar physicochemical properties (glass transition temperature, and structural relaxation time), but also similar molecular conformational distributions were observed for all prepared CEL amorphous systems. Hence, despite their different recrystallization behaviors, the three amorphous forms of CEL did not show any signs of polyamorphism.

Not just a pretty picture: Mapping Leaf Area Index at 10 m resolution using Sentinel-2

Achieving the Global Climate Observing System goal of 10 m resolution leaf area index (LAI) maps is critical for applications related to climate adaptation, sustainable agriculture, and ecosystem monitoring. Five strategies for producing 10 m LAI maps from Sentinel-2 (S2) imagery are evaluated: i. bi-cubic interpolation of 20 m resolution S2 LAI maps from the Simplified Level 2 Prototype Processor Version 1 (SL2PV1) as currently performed by the Sentinel Applications Platform (SNAP), ii. applying SL2PV1 to S2 reflectance bands spatially downscaled to 10 m using bi-cubic interpolation (BICUBIC), iii. Applying SL2PV1 to S2 reflectance bands spatially downscaled to 10 m using Area to Point Regression Kriging (ATPRK), iv. using a recalibrated version of SL2PV1 (SL2PV2) requiring only three S2 10m bands, and iv) a novel use of the previously developed Active Learning Regularization (ALR) approach to locally approximate the SL2PV1 algorithm using only 10 m bands.Algorithms were assessed in terms of per-pixel accuracy and spatial metrics when comparing 10 m LAI maps produced using either actual S2 imagery or S2 imagery synthesized from airborne hyperspectral imagery to reference 10 m LAI maps traceable to in-situ fiducial reference measurements at 10 sites across the continental US. ATPRK and ALR algorithms had the lowest precision error of ∼0.15 LAI, compared to 0.19 LAI for SNAP and BICUBIC and 0.35 LAI for SL2PV2, and ranked highest in terms of local correlation and Structural Similarity Index measure as well as qualitative agreement with reference maps. SL2PV2 LAI showed evidence of saturation over forests related to decreased sensitivity of input visible reflectance. All algorithms had a similar uncertainty of ∼0.55 LAI compared to traceable reference maps, due to the trade-off between bias and precision. However, ATPRK and ALR uncertainty reduced to 0.11 LAI and 0.16 LAI, respectively, when compared to reference maps that ignored canopy clumping. These results suggest that both ATPRK and ALR are suitable for producing 10 m S2 LAI maps assuming bias due to local clumping can be corrected in the underlying SL2PV1 algorithm.

Global–local consistent semi-supervised segmentation of histopathological image with different perturbations

A histopathological image is a microscopic image applied to examine cellular and tissue structures and identify any abnormalities or disease processes. Histopathological image segmentation is a prerequisite step for analyzing histopathological images that can divide an image into meaningful regions or objects to accurately classify and analyze tissue structures, cellular regions, or particular histological entities. However, the existing deep learning based pathological image segmentation methods require huge annotation efforts from the pathologists, which is labor-intensive and time-consuming. In this scenario, it has become a hotspot to leverage abundantly available unlabeled data to help learn segmentation models given limited labeled data. In this paper, we propose a global–local consistent semi-supervised segmentation (GLCS) model that enforce the consistency of the segmentation results with weak and strong perturbations on unlabeled data. In GLCS, we firstly generate different weak perturbations for each unlabeled sample, and then add a regularization term to ensure the segmentation consistency among different weak perturbations. Next, different from the existing studies applying the regression methods to match the segmentation results among different perturbations, our methods are based on the generative adversarial learning that can keep the global structure consistency among unlabeled data with different strength of perturbations. Finally, we also add a patch-correlation based regularization term to preserve the local structure similarity among different perturbations images. We validate our GLCS on three datasets, i.e. Glas, Crag and MoNuSeg. The experimental results show that our method can achieve to the dice ratio of 90.35, 82.61 and 81.60 with 1:1 proportion of labeled data, which are significantly superior to the state-of-the-art semi-supervised histopathological image segmentation methods. Our code is public available at https://github.com/ISBELLAG/GLCS.

A Robust Mismatch Removal Method for Image Matching Based on the Fusion of the Local Features and the Depth

Feature point matching is a fundamental task in computer vision such as vision simultaneous localization and mapping (VSLAM) and structure from motion (SFM). Due to the similarity or interference of features, mismatches are often unavoidable. Therefore, how to eliminate mismatches is important for robust matching. Smoothness constraint is widely used to remove mismatch, but it cannot effectively deal with the issue in the rapidly changing scene. In this paper, a novel LCS-SSM (Local Cell Statistics and Structural Similarity Measurement) mismatch removal method is proposed. LCS-SSM integrates the motion consistency and structural similarity of a local image block as the statistical likelihood of matched key points. Then, the Random Sampling Consensus (RANSAC) algorithm is employed to preserve the isolated matches that do not satisfy the statistical likelihood. Experimental and comparative results on the public dataset show that the proposed LCS-SSM can effectively and reliably differentiate true and false matches compared with state-of-the-art methods, and can be used for robust matching in scenes with fast motion, blurs, and clustered noise.

Focus on informative graphs! Semi-supervised active learning for graph-level classification

Graph-level classification is a critical problem in social analysis and bioinformatics. Since annotated labels are typically costly, we intend to study this challenging task in semi-supervised scenarios with limited budgets. Inspired by the fact that active learning is capable of interactively querying an oracle to annotate a small number of informative examples in the unlabeled dataset, we develop a novel Semi-supervised active learning framework termed GraphSpa for graph-level classification. To make the most of labeling budgets, we propose an effective unlabeled data selection strategy that takes both local similarity and global semantic structure into account. Specifically, we first construct an adaptive queue with labeled samples and select informative samples that have a low degree of similarity to the queue using the Min-Max principle from the local view. Further, we introduce class prototypes and select samples with a large predictive loss discrepancy from the global view. To harness the full potential of unlabeled data, we develop a semi-supervised active learning framework on the basis of our fusion selection strategy coupled with graph contrastive learning during active learning. The effectiveness of our GraphSpa is validated against state-of-the-art methods through experimental results on diverse real-world benchmark datasets.

DP-GCN: Node Classification by Connectivity and Local Topology Structure on Real-World Network

Node classification is to predict the class label of a node by analyzing its properties and interactions in a network. We note that many existing solutions for graph-based node classification only consider node connectivity but not the node’s local topology structure. However, nodes residing in different parts of a real-world network may share similar local topology structures. For example, local topology structures in a payment network may reveal sellers’ business roles (e.g., supplier or retailer). To model both connectivity and local topology structure for better node classification performance, we present DP-GCN, a dual-path graph convolution network. DP-GCN consists of three main modules: (i) a C-GCN module to capture the connectivity relationships between nodes, (ii) a T-GCN module to capture the topology structure similarity among nodes, and (iii) a multi-head self-attention module to align both properties. We evaluate DP-GCN on seven benchmark datasets against diverse baselines to demonstrate its effectiveness. We also provide a case study of running DP-GCN on three large-scale payment networks from PayPal, a leading payment service provider, for risky seller detection. Experimental results show DP-GCN’s effectiveness and practicability in large-scale settings. PayPal’s internal testing also shows DP-GCN’s effectiveness in defending against real risks from transaction networks.

Medical image fusion method based on saliency measurement improvement and local structure similarity correction

Medical image fusion is an important tool in medical diagnosis, because the quality of the fused image is closely related to the diagnosis result. However, the approaches of highlighting details through the acquisition of high-contrast lead to information distortion in the fused image and then increase the misdiagnosis rate. In order to solve the problem, a medical image fusion method based on saliency measurement improvement and local structural similarity correction is proposed in this paper. First, multi-level decomposition latent low-rank representation (MDLatLRR) combined with nonsubsampled shearlet transformation (NSST) is presented to decompose source images to obtain high SNR base layers and multi-scale detail layers. Then, the saliency measurement improvement with the source image mask filtering is advanced for base layer fusion rule. Specially, after the base layer is refined by the source image mask for the salient area extraction, the salient area is refreshed by its mean gray to calculate the fusion coefficient. Finally, a correction method based on structural similarity index measure (SSIM) is put forward to deal with the information distortion in the initial fused image, in which the correction weight is acquired through the weighted structural similarity between the initial fused image and the source ones. Experiments show that our method has excellent subjective visual effects, and outperforms the other states-of-the-art methods in objective indexes.

Crosscorrelation-based dynamic time warping and its application in wave equation reflection traveltime inversion

Crosscorrelation and dynamic time warping (DTW) are ubiquitous in time-shift estimation. However, the small-shift limitation of crosscorrelation and the instability and high sensitivity to noise of DTW seriously hinder their applications in complex situations. In this study, we develop a method called crosscorrelation-based DTW (CDTW) to address these issues. Our method constructs error matrices by local crosscorrelation instead of Euclidean distance to minimize the sensitivity to noise. The new error matrices are calculated in local windows and contain local structure similarity information of the two input signals. It improves the stability of the algorithm and makes the CDTW method less sensitive to noise and amplitude modulation. Our method estimates the time-varying shift using dynamic programming as the conventional DTW after the new error matrix is formulated. Numerical tests on pairs of signals and seismic images prove that our method can accurately estimate time shifts in cases of time-varying amplitude modulation and strong random noise contamination. Finally, we apply the CDTW method to wave equation reflection traveltime inversion (WRTI) and develop a CDTW-based WRTI method. Synthetic and field applications prove that this method can construct good background velocity models with the reliable reflection traveltime shifts produced by the CDTW method.

Semantics-preserved Graph Siamese Representation Learning

Currently, the expressive power of momentum-driven graph Siamese representation learning is mainly constrained by flawed positive selection strategies and data augmentation strategies. To solve these issues, we propose a Semantics-preserved Graph Siamese Representation Learning method named SGSRL. Firstly, we propose a local structure-aware positive selection strategy to reduce the generation of false positive samples. Specifically, the local structural similarity of nodes is obtained through the comparison between corresponding sampled subgraphs, where the anchor node’s subgraph is pooled to simplify the calculation. In addition, we propose a class information-aware feature augmentation method. Compared with directly modifying the inputs of the original graph, augmentation in the feature space is more controllable and intuitive. Specifically, we design an auxiliary contrastive learning task to train a class vector generator, and then overlay the class-oriented augmentation vectors generated by this generator onto the randomly augmented feature vectors. This joint feature augmentation strategy increases the variance between positive samples without changing the semanticity of nodes. To validate the superiority of our method, we conduct experiments on five real graph datasets for both node classification and node clustering tasks. The results demonstrate that our proposed SGSRL always outperforms state-of-art baselines.

Feature matching of remote‐sensing images based on bilateral local–global structure consistency

AbstractThe goal of feature matching is to establish accurate correspondences between feature points in different images depicting the same scene. To address the polymorphism of local structures, the authors propose a mismatch removal method using bilateral local–global structural consistency. This method incorporates the problem of mismatch removal into the framework of graph matching, constructs a global affinity matrix using local structural similarity and global affine transformation consistency, and optimizes it using a constrained integer quadratic programming method. To comprehensively describe the local structure, the signature quadratic form distance (SQFD) is used to measure the consistency of the neighbourhood structure. Specifically, the weights of edges are constructed based on the SQFD of the local structure, while the matching correctness of nodes and edges between the two graphs is described using local vector similarity. Furthermore, the consistency of the global affine transformation is evaluated by assessing the consistency of the local neighbourhood affine transformation between different corresponding point pairs. In estimating the local affine transformation, a bilateral correction is performed using a total least‐squares (TLS) algorithm to measure the similarity of nodes between the two different graphs. Experimental results demonstrate that the proposed algorithm outperforms state‐of‐the‐art methods in terms of accuracy and effectiveness.

Enhancing Cross-Lingual Entity Alignment in Knowledge Graphs through Structure Similarity Rearrangement.

Cross-lingual entity alignment in knowledge graphs is a crucial task in knowledge fusion. This task involves learning low-dimensional embeddings for nodes in different knowledge graphs and identifying equivalent entities across them by measuring the distances between their representation vectors. Existing alignment models use neural network modules and the nearest neighbors algorithm to find suitable entity pairs. However, these models often ignore the importance of local structural features of entities during the alignment stage, which may lead to reduced matching accuracy. Specifically, nodes that are poorly represented may not benefit from their surrounding context. In this article, we propose a novel alignment model called SSR, which leverages the node embedding algorithm in graphs to select candidate entities and then rearranges them by local structural similarity in the source and target knowledge graphs. Our approach improves the performance of existing approaches and is compatible with them. We demonstrate the effectiveness of our approach on the DBP15k dataset, showing that it outperforms existing methods while requiring less time.

Similarity-Driven Topology Optimization for Statics and Crash via Energy Scaling Method

Abstract Topology optimization (TO) is used in the initial design phase to optimize certain objective functions under given boundary conditions by finding suitable material distributions in a specified design domain. Currently available methods in the industry work very efficiently to get topologically optimized design concepts under static and dynamic load cases. However, conventional methods do not address the designer’s preferences about the final material layout in the optimized design. In practice, the final design might be required to have a certain degree of local or global structural similarity with an already present good reference design because of economic, manufacturing, and assembly limitations or the desire to re-use parts in different systems. In this article, a heuristic energy scaling method (ESM) for similarity-driven TO under static as well as dynamic loading conditions is presented and thoroughly evaluated. A 2D cantilever beam under static point load is used to show that the proposed method can be coupled with gradient-based and also heuristic, nongradient methods to get designs of varying similarity with respect to a reference design. Further testing of the proposed method for similarity-driven TO on a 2D crash test case and a large-scale 3D hood model of a car body indicates the effectiveness of the method for a wide range of problems in the industry. Finally, the application of similarity-driven TO is further extended to show that ESM also has the potential for sensitivity analysis of performance with respect to the extension of design domain.

Predicting Scientist Collaboration by Multiple Motif Features

Scientist collaboration is of great significance for knowledge production and scientific development, and the prediction of connection and the intensity in collaboration networks are essential to understand collaboration relationships between scientists. In previous studies, most scholars only use local structure similarity to infer scientist collaboration modes, which leads to failure to accurately predict collaboration relationships between scientists. In this study, we propose a prediction method to identify missing links and link weight by using multiple motif features. The experimental results show that the highest improvement of performance in link prediction is 13.5%, and 86.8% in weight prediction. In addition, the correlation analysis on multiple motif features reveals topology correlation between different scientist collaboration modes. Our finding is helpful to predict link possibility and tie strength in scientist collaboration networks more accurately and understand deeply the evolution pattern of collaboration networks among scientists.

Calibration of local chemical pressure by optical probe.

Chemical stabilization of a high-pressure metastable state is a major challenge for the development of advanced materials. Although chemical pressure (Pchem) can effectively simulate the effect of physical pressure (Pphy), experimental calibration of the pressure passed to local structural motifs, denoted as local chemical pressure (Pchem-Δ) which significantly governs the function of solid materials, remains absent due to the challenge of probing techniques. Here we establish an innovative methodology to experimentally calibrate the Pchem-Δ and build a bridge between Pchem and Pphy via an optical probe strategy. Site-selective Bi3+-traced REVO4 (RE=Y, Gd) is adopted as a prototype to introduce Bi3+ optical probes and on-site sense of the Pchem-Δ experienced by the REO8 motif. The cell compression of RE0.98Bi0.02VO4 under Pphy is chemically simulated by smaller-ion substitution (Sc3+ → RE3+) in RE0.98-xScxBi0.02VO4. The consistent red shift (Δλ) of the emission spectra of Bi3+, which is dominated by locally pressure-induced REO8 dodecahedral variation in RE0.98Bi0.02VO4 (Pphy) and RE0.98-xScxBi0.02VO4 (Pchem-Δ), respectively, is evidence of their similar pressure-dependent local structure evolution. This innovative Δλ-based experimental calibration of Pchem-Δ in the crystal-field dimension portrays the anisotropic transmission of Pchem to the local structure and builds a bridge between Pchem-Δ and Pphy to guide a new perspective for affordable and practical interception of metastable states.

Large eddy simulation of OME3 and OME4 spray combustion under heavy-duty conditions

The synthetic diesel fuel (Poly-)oxymethylene ether (OME) has various interesting properties for the transport sector. OME reduces emissions in direct injection (DI) diesel engines, e.g., soot and NOx, and is considered CO2 neutral. While most studies have examined a mixture of OMEs with different chain lengths, recent studies have focused on neat OME3 and OME4. In the literature similar chemical ignition delay times were shown under homogeneous conditions and significant differences were reported in their thermophysical properties. Furthermore, differences between OME3 and OME4 in terms of the mixture formation were shown on a heavy-duty injector under non-reactive conditions. The question of how this mixture formation affects the ignition and flame characteristics of neat OME3 and OME4 spray flames remains unresolved. To address this topic, this study conducts a combined experimental and numerical investigation under inert and reactive spray conditions for OME3 and OME4 on a heavy-duty injector. Large Eddy Simulations (LESs) and high-pressure, high-temperature spray chamber measurements are conducted. The distinct differences between neat OME3 and OME4 are analyzed along the cause–effect chain comprising the mixture formation, ignition behavior and its effects on the overall heat release rate. Based on the differences in the thermophysical properties, e.g., density and vapor pressure, the investigations in inert conditions reveal differences in the liquid penetration and mixture formation. The reactive spray flame measurements and simulations show a significant offset in the ignition delay time for OME3 and OME4. An explanation is found in the significantly different distribution of the scalar dissipation rate due to the differences in the mixture formation. Despite the differing ignition delay times, similar local flame structures are found for both fuels.

A new local structural similarity fusion-based thresholding method for homomorphic ultrasound image despeckling in NSCT domain

Ultrasound is a diagnostic imaging technique to detect various illnesses related to the body's internal organs. It is a non-invasive, safe, cost-effective, and simple imaging modality that uses high-frequency sound waves and their reflection from internal organs. However, a key drawback of using ultrasound imaging tools is that they are often contaminated by multiplicative speckle noise, which can reduce the image resolution and contrast and complicates the diagnosis of the tiny structure of lesions. Extensive studies have shown that eliminating speckle noise is a necessary pre-processing task for computer-assisted ultrasound image systems. This paper proposes a novel method for denoising, weighted thresholding, and fusion of ultrasound images in the Non-Subsampled Contourlet Transformation (NSCT) domain. The method utilizes (a) The Bayesian and Bivariate shrinkage rule in the NSCT domain, (b) A new local structural similarity-fusion-based homomorphic ultrasound image despeckling method in the NSCT domain, and (c) A weights estimation approach based on a bilateral method that helps improve noise suppression and edge preservation results. The performance of the proposed algorithm is evaluated in terms of mean squared error (MSE), peak signal-to-noise ratio, structural similarity index (SSIM), speckle suppression index, signal-to-MSE ratio, and speckle signal-to-noise ratio. Extensive computer simulation results show that the presented method performs better in subjective visual inspection than existing state-of-the-art despeckling techniques.