Structural Information Research Articles

Monocular vision-based structural out-of-plane motion estimation using a deep learning method

Computer vision-based displacement estimation methods can estimate structural motion from videos, but face challenges in estimating out-of-plane motion. Conventional vision-based methods employ either multi-view camera systems or structural model-aided monocular vision methods to estimate structural out-of-plane motion. Nonetheless, the utilization of multi-view camera systems leads to additional costs and stringent requirements for overlapping views, while structural model-aided methods require comprehensive structural information. To achieve convenient and accurate out-of-plane structural displacement measurement, this study presented a monocular vision-based measurement method using a deep learning technique. Firstly, a deep learning model was employed to estimate the global depth matrix of images. The relative depth of the region of interest (ROI) was then estimated through pyramid-accelerated tracking and relative depth matrix weighting. To convert the relative depth into actual out-of-plane motion, the scale factor was calculated through least squares fit. The performance of the proposed method was validated through static and dynamic tests. The results indicated that the proposed approach yielded reasonable out-of-plane motion estimations, with correlation coefficients with displacement sensor measurements over 0.93. The proposed method therefore offered a cost-effective and convenient alternative for structural out-of-plane motion estimation.

CT and MRI Image Fusion via Coupled Feature-Learning GAN

The fusion of multimodal medical images, particularly CT and MRI, is driven by the need to enhance the diagnostic process by providing clinicians with a single, comprehensive image that encapsulates all necessary details. Existing fusion methods often exhibit a bias towards features from one of the source images, making it challenging to simultaneously preserve both structural information and textural details. Designing an effective fusion method that can preserve more discriminative information is therefore crucial. In this work, we propose a Coupled Feature-Learning GAN (CFGAN) to fuse the multimodal medical images into a single informative image. The proposed method establishes an adversarial game between the discriminators and a couple of generators. First, the coupled generators are trained to generate two real-like fused images, which are then used to deceive the two coupled discriminators. Subsequently, the two discriminators are devised to minimize the structural distance to ensure the abundant information in the original source images is well-maintained in the fused image. We further empower the generators to be robust under various scales by constructing a discriminative feature extraction (DFE) block with different dilation rates. Moreover, we introduce a cross-dimension interaction attention (CIA) block to refine the feature representations. The qualitative and quantitative experiments on common benchmarks demonstrate the competitive performance of the CFGAN compared to other state-of-the-art methods.

A Collaborative Digitisation and Curatorial Project, Highlighting a Unique Collection and the Latimer Core Data Standard

Sir Joseph Banks is remembered for being a long-standing president of the Royal Society, the unofficial first director of Kew gardens and the pioneering naturalist on Captain James Cook’s great voyage onboard the Endeavour, to observe the transit of Venus and search for an undiscovered southern continent (British Museum (Natural History) 1906). Much of Bank’s life is well documented but his surviving entomology collection has never been accurately catalogued. The Banks Collection at the Natural History Museum (NHM) London is an historic assemblage of insect specimens (Fig. 1, British Museum (Natural History) 1906). It includes specimens collected by Banks and others acquired through a world-wide network of collectors. During his lifetime, Banks shared specimens with his associates and gave many specimens to Dr. William Hunter and Johan Christian Fabricius. After his death, the remaining collection was donated to the Linnean Society and later passed to the British Museum in 1863. The Banks Collection has both historical and cultural value and continues to be a relevant research tool. This is largely because Fabricius, a student of Carl Linnaeus, described many new species from the collection. Consequently, the collection contains many taxonomically important type specimens. The number of specimens in the collection is unknown but estimated to be approximately 4000. The NHM is digitising the collection with the generous support of the Charles Hayward Foundation. The collection is housed in 55 entomological glass-topped drawers, albeit not the original drawers. When conserving historical material, there is an argument to leave everything in its original state, but after much consideration, the curatorial team decided—for the long-term preservation of the collection—the specimens should be rehoused into plastazote®-lined drawers keeping the original layout of the specimens and replacing the current cork-lined drawers as part of the digitisation process. High-resolution images are taken of every specimen with its associated labels. The information on the labels is recorded for each specimen and a barcode added. As of August 2024, 3,300 specimens have been digitised and 30 of the 55 drawers recurated. More than 5000 high-resolution images have been taken (Fig. 2) and 438 labels have been transcribed for almost 1200 specimens. This project is using a new Biodiversity Information Standards (TDWG) data standard, Latimer Core, designed to support the representation and discovery of natural science collections (Woodburn et al. 2022). Latimer Core is intended to be complimentary to specimen-level standards such as Darwin Core (Darwin Core Task Group 2009), providing a way to structure and share higher-level information about groups of collection objects, from whole-museum collections through thematic and historic collections, to the contents of a single drawer. This is useful for collections with lots of associated data, fragile specimens or when displacement and disassociation of information is a concern. Unlike modern specimens, with data labels containing information on collecting event and associated persons, specimens in the Banks collection have few original labels. It was common practice that labels were temporary data storage and disposed of once the data was published. A replacement label was provided after publication (Fig. 2). The publication provides species description, geographic origin and often the name of the collection containing the specimen. Additional labels were subsequently added by curators and researchers. Many specimens have no data labels (Fig. 3). Unravelling important information about individual specimens, species and groups of specimens in this collection, including taxonomic, type status and origin will be supported by Latimer Core, as it allows a more holistic approach compared to only using specimen-level data. We can better incorporate data derived from various sources such as published data, illustrations and personal correspondence to learn about the individual and groups of specimens, the reverse of the usual workflow. Digitising this collection will improve access through digital records (specimen/drawer images, transcribed labels, publication references) reducing the need for physical examination and risk of damage. Whole-drawer digitisation in addition to specimen-level imaging provides information on the organisation and display of a collection. A collection of this nature demands minimal handling and the best storage and collections management procedures to ensure its survival for future generations. However, its significance commands a continued interest by a wide and varied audience. By digitising this collection, we will improve its physical housing, increase its accessibility without compromising the specimens for the future, and support the publication of a comprehensive catalogue of the collection.

Metal–Organic Framework‐Induced Rh Monocoodination on Diphosphine Ligand Enables Catalytic Hydroformylation of Aliphatic Olefins at Room Temperature and Pressure

AbstractPersistent challenges in hydroformylation of olefins include controlling regioselectivity, particularly for short aliphatic olefins and conducting reactions under ambient conditions. We report here the synthesis of monophosphine–Rh complexes on a typical chelated diphosphine ligand mediated by a Zr‐MOF through isolating a pair of phosphorus atoms. We demonstrate that single‐crystal X‐ray diffraction can elucidate the structural transformation of the Rh catalyst during olefin hydroformylation, providing valuable information on active site reconstruction during catalysis. The Rh‐MOF catalyst demonstrates excellent catalytic and recyclable performance in the hydroformylation of short aliphatic olefins with linear to branched ratios of up to 99 : 1. Due to the framework‘s capacity to adsorb and concentrate gases, the catalytic reactions occur under room temperature and pressure, eliminating the need for the high temperature and pressures typically required in homogeneous systems. This study show that Zr–MOF can be a unique platform for synthesizing unusual catalytic species that cannot exist in solutions for meaningful chemical transformations and elucidate valuable structural information pertaining to metal‐based catalysis.

Pulse EPR Methods in the Angstrom to Nanometre Scale Shed Light on the Conformational Flexibility of a Fluoride Riboswitch.

Riboswitches control gene regulation upon external stimuli such as environmental factors or ligand binding. The fluoride sensing riboswitch from Thermotoga petrophila is a complex regulatory RNA proposed to be involved in resistance to F- cytotoxicity. The details of structure and dynamics underpinning the regulatory mechanism are currently debated. Here we demonstrate that a combination of pulsed electron paramagnetic resonance (ESR/EPR) spectroscopies, detecting distances in the angstrom to nanometre range, can probe distinct regions of conformational flexibility in this riboswitch. PELDOR (pulsed electron-electron double resonance) revealed a similar preorganisation of the sensing domain in three forms, i.e. the free aptamer, the Mg2+-bound apo, and the F--bound holo form. 19F ENDOR (electron-nuclear double resonance) was used to investigate the active site structure of the F--bound holo form. Distance distributions without a priori structural information were compared with in silico modelling of spin label conformations based on the crystal structure. While PELDOR, probing the periphery of the RNA fold, revealed conformational flexibility of the RNA backbone, ENDOR indicated low structural heterogeneity at the ligand binding site. Overall, the combination of PELDOR and ENDOR with sub-angstrom precision gave insight into structural organisation and flexibility of a riboswitch, not easily attainable by other biophysical techniques.

DiffLense: A Conditional Diffusion Model for Super-Resolution of Gravitational Lensing Data

Abstract Gravitational lensing data is frequently collected at low resolution due to instrumental limitations and observing conditions. Machine learning-based super-resolution techniques offer a method to enhance the resolution of these images, enabling more precise measurements of lensing effects and a better understanding of the matter distribution in the lensing system. This enhancement can significantly improve our knowledge of the distribution of mass within the lensing galaxy and its environment, as well as the properties of the background source being lensed. Traditional super-resolution techniques typically learn a mapping function from lower-resolution to higher-resolution samples. However, these methods are often constrained by their dependence on optimizing a fixed distance function, which can result in the loss of intricate details crucial for astrophysical analysis. In this work, we introduce DiffLense, a novel super-resolution pipeline based on a conditional diffusion model specifically designed to enhance the resolution of gravitational lensing images obtained from the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP). Our approach adopts a generative model, leveraging the detailed structural information present in Hubble Space Telescope (HST) counterparts. The diffusion model, trained to generate HST data, is conditioned on HSC data pre-processed with denoising techniques and thresholding to significantly reduce noise and background interference. This process leads to a more distinct and less overlapping conditional distribution during the model's training phase. We demonstrate that DiffLense outperforms existing state-of-the-art single-image super-resolution techniques, particularly in retaining the fine details necessary for astrophysical analyses.

Pulse EPR Methods in the Angstrom to Nanometre Scale Shed Light on the Conformational Flexibility of a Fluoride Riboswitch

Riboswitches control gene regulation upon external stimuli such as environmental factors or ligand binding. The fluoride sensing riboswitch from Thermotoga petrophila is a complex regulatory RNA proposed to be involved in resistance to F‐ cytotoxicity. The details of structure and dynamics underpinning the regulatory mechanism are currently debated. Here we demonstrate that a combination of pulsed electron paramagnetic resonance (ESR/EPR) spectroscopies, detecting distances in the angstrom to nanometre range, can probe distinct regions of conformational flexibility in this riboswitch. PELDOR (pulsed electron‐electron double resonance) revealed a similar preorganisation of the sensing domain in three forms, i.e. the free aptamer, the Mg2+‐bound apo, and the F‐‐bound holo form. 19F ENDOR (electron‐nuclear double resonance) was used to investigate the active site structure of the F‐‐bound holo form. Distance distributions without a priori structural information were compared with in silico modelling of spin label conformations based on the crystal structure. While PELDOR, probing the periphery of the RNA fold, revealed conformational flexibility of the RNA backbone, ENDOR indicated low structural heterogeneity at the ligand binding site. Overall, the combination of PELDOR and ENDOR with sub‐angstrom precision gave insight into structural organisation and flexibility of a riboswitch, not easily attainable by other biophysical techniques.

Analyzing the impact of conversation structure on predicting persuasive comments online

AbstractThe topic of persuasion in online conversations has social, political and security implications; as a consequence, the problem of predicting persuasive comments in online discussions is receiving increasing attention in the literature. Following recent advancements in graph neural networks, we analyze the impact of conversation structure in predicting persuasive comments in online discussions. We evaluate the performance of artificial intelligence models receiving as input graphs constructed on the top of online conversations sourced from the “Change My View” Reddit channel. We experiment with different graph architectures and compare the performance on graph neural networks, as structure-based models, and dense neural networks as baseline models. Experiments are conducted on two tasks: (1) persuasive comment detection, aiming to predict which comments are persuasive, and (2) influence prediction, aiming to predict which users are persuasive. The experimental results show that the role of the conversation structure in predicting persuasiveness is strongly dependent on its graph representation given as input to the graph neural network. In particular, a graph structure linking only comments belonging to the same speaker in the conversation achieves the best performance in both tasks. This structure outperforms both the baseline model, which does not consider any structural information, and structures linking different speakers’ comments with each other. Specifically, the F1 score of the best performing model is 0.58, which represents an improvement of 5.45% over the baseline model (F1 score of 0.55) and 7.41% over the model linking different speakers’ comments (F1 score of 0.54).

Developing a BIM based digital twin system for structural health monitoring of civil infrastructure

Abstract The utilization of building information modeling (BIM) within digital technology facilitates the creation of three-dimensional representations for monitoring data in large-scale civil infrastructure. In response to the need for intelligent structural management, this study establishes a structural health monitoring (SHM) system and foundational framework based on digital twins. This framework integrates information from various sources and facilitates collaborative efforts for structural operation and maintenance. Additionally, the SHM system integrates actual monitoring measurements and early warning mechanisms to consolidate multi-source monitoring data with BIM. Through real-time analysis, the system provides insights into the operational status of bridges, capturing geometric, physical, and performance evolution characteristics. To construct the system, engineering challenges are initially digitized, with appropriate sensors deployed on real bridge structures to monitor dynamic (acceleration) and static (strain, displacement) physical information during bridge operation. Subsequently, through wireless communication and data storage technologies, the monitored physical data serves as input for mode identification and early warning algorithms, facilitating the acquisition of structural performance information. Finally, three-dimensional display technology enables real-time calculation and rendering of BIM models, fostering the exchange and interaction of monitoring and BIM information, thus enhancing the intelligence of SHM system.

PCP-GC-LM: single-sequence-based protein contact prediction using dual graph convolutional neural network and convolutional neural network.

Recently, the process of evolution information and the deep learning network has promoted the improvement of protein contact prediction methods. Nevertheless, still remain some bottleneck: (1) One of the bottlenecks is the prediction of orphans and other fewer evolution information proteins. (2) The other bottleneck is the method of predicting single-sequence-based proteins mainly focuses on selecting protein sequence features and tuning the neural network architecture, However, while the deeper neural networks improve prediction accuracy, there is still the problem of increasing the computational burden. Compared with other neural networks in the field of protein prediction, the graph neural network has the following advantages: due to the advantage of revealing the topology structure via graph neural network and being able to take advantage of the hierarchical structure and local connectivity of graph neural networks has certain advantages in capturing the features of different levels of abstraction in protein molecules. When using protein sequence and structure information for joint training, the dependencies between the two kinds of information can be better captured. And it can process protein molecular structures of different lengths and shapes, while traditional neural networks need to convert proteins into fixed-size vectors or matrices for processing. Here, we propose a single-sequence-based protein contact map predictor PCP-GC-LM, with dual-level graph neural networks and convolution networks. Our method performs better with other single-sequence-based predictors in different independent tests. In addition, to verify the validity of our method against complex protein structures, we will also compare it with other methods in two homodimers protein test sets (DeepHomo test dataset and CASP-CAPRI target dataset). Furthermore, we also perform ablation experiments to demonstrate the necessity of a dual graph network. In all, our framework presents new modules to accurately predict inter-chain contact maps in protein and it's also useful to analyze interactions in other types of protein complexes.

SSGU-CD: A combined semantic and structural information graph U-shaped network for document-level Chemical-Disease interaction extraction

Document-level interaction extraction for Chemical-Disease is aimed at inferring the interaction relations between chemical entities and disease entities across multiple sentences. Compared with sentence-level relation extraction, document-level relation extraction can capture the associations between different entities throughout the entire document, which is found to be more practical for biomedical text information. However, current biomedical extraction methods mainly concentrate on sentence-level relation extraction, making it difficult to access the rich structural information contained in documents in practical application scenarios. We put forward SSGU-CD, a combined Semantic and Structural information Graph U-shaped network for document-level Chemical-Disease interaction extraction. This framework effectively stores document semantic and structure information as graphs and can fuse the original context information of documents. Using the framework, we propose a balanced combination of cross-entropy loss function to facilitate collaborative optimization among models with the aim of enhancing the ability to extract Chemical-Disease interaction relations. We evaluated SSGU-CD on the document-level relation extraction dataset CDR and BioRED, and the results demonstrate that the framework can significantly improve the extraction performance.

Masked contrastive graph representation learning for age estimation

Age estimation of face images is a crucial task with various practical applications in areas such as video surveillance and Internet access control. While deep learning-based age estimation methods like Convolutional Neural Networks (CNN), Multilayer Perceptrons (MLP), and Transformers have shown remarkable performance, they have limitations when modeling complex or irregular objects, and often contain redundant information. Although existing graph neural networks (GNNs) can alleviate this issue, they only considers the structural information of the image and ignores the semantic features, thus the feature representation capability of graph nodes is limited. To address these issues, this paper proposes a novel Masked Contrastive Graph Representation Learning (MCGRL) method for accurate age estimation of face images. Our approach leverages CNN to extract semantic features of the image, which are then partitioned into patches that serve as nodes in the graph. We use a masked graph convolutional network (GCN) to derive image-based node representations that capture rich structural information. Finally, we incorporate multiple losses to explore the complementary relationship between structural information and semantic features, which improves the feature representation capability of GCN. Experimental results on real-world face image datasets demonstrate the superiority of our proposed method over other state-of-the-art age estimation approaches. Our code is available at https://github.com/yuntaoshou/MCGRL

Graph Contrastive Learning with Node-Level Accurate Difference

Graph contrastive learning (GCL) has attracted extensive research interest due to its powerful ability to capture latent structural and semantic information of graphs in a self-supervised manner. Existing GCL methods commonly adopt predefined graph augmentations to generate two contrastive views. Subsequently, they design a contrastive pretext task between these views with the goal of maximizing their agreement. These methods assume the augmented graph can fully preserve the semantics of the original. However, typical data augmentation strategies in GCL, such as random edge dropping, may alter the properties of the original graph. As a result, previous GCL methods overlooked graph differences, potentially leading to difficulty distinguishing between graphs that are structurally similar but semantically different. Therefore, we argue that it is necessary to design a method that can quantify the dissimilarity between the original and augmented graphs to more accurately capture the relationships between samples. In this work, we propose a novel graph contrastive learning framework, named Accurate Difference-based Node-Level Graph Contrastive Learning (DNGCL), which helps the model distinguish similar graphs with slight differences by learning node-level differences between graphs. Specifically, we train the model to distinguish between original and augmented nodes via a node discriminator and employ cosine dissimilarity to accurately measure the difference between each node. Furthermore, we employ multiple types of data augmentation commonly used in current GCL methods on the original graph, aiming to learn the differences between nodes under different augmentation strategies and help the model learn richer local information. We conduct extensive experiments on six benchmark datasets and the results show that our DNGCL outperforms most state-of-the-art baselines, which strongly validates the effectiveness of our model.

LearnMSA2: deep protein multiple alignments with large language and hidden Markov models.

For the alignment of large numbers of protein sequences, tools are predominant that decide to align two residues using only simple prior knowledge, e.g. amino acid substitution matrices, and using only part of the available data. The accuracy of state-of-the-art programs declines with decreasing sequence identity and when increasingly large numbers of sequences are aligned. Recently, transformer-based deep-learning models started to harness the vast amount of protein sequence data, resulting in powerful pretrained language models with the main purpose of generating high-dimensional numerical representations, embeddings, for individual sites that agglomerate evolutionary, structural, and biophysical information. We extend the traditional profile hidden Markov model so that it takes as inputs unaligned protein sequences and the corresponding embeddings. We fit the model with gradient descent using our existing differentiable hidden Markov layer. All sequences and their embeddings are jointly aligned to a model of the protein family. We report that our upgraded HMM-based aligner, learnMSA2, combined with the ProtT5-XL protein language model aligns on average almost 6% points more columns correctly than the best amino acid-based competitor and scales well with sequence number. The relative advantage of learnMSA2 over other programs tends to be greater when the sequence identity is lower and when the number of sequences is larger. Our results strengthen the evidence on the rich information contained in protein language models' embeddings and their potential downstream impact on the field of bioinformatics. Availability and implementation: https://github.com/Gaius-Augustus/learnMSA, PyPI and Bioconda, evaluation: https://github.com/felbecker/snakeMSA.

Cardiac Organoids: A 3D Technology for Disease Modeling and Drug Screening.

Cardiovascular diseases remain the leading cause of death worldwide; therefore, there is increasing attention to developing physiological-related in vitro cardiovascular tissue models suitable for personalized healthcare and preclinical test. Recently, more complex and powerful in vitro models have emerged for cardiac research. Human cardiac organoids (HCOs) are three-dimensional (3D) cellular constructs similar to in vivo organs. They are derived from pluripotent stem cells and can replicate the structure, function, and biogenetic information of primitive tissues. High-fidelity HCOs are closer to natural human myocardial tissue than animal and cell models to some extent, which helps to study better the development process of the heart and the occurrence of related diseases. In this review, we introduce the methods for constructing HCOs and the application of them, especially in cardiovascular disease modeling and cardiac drug screening. In addition, we propose the prospects and limitations of HCOs. In summary, we have introduced the research progress of HCOs and described their innovation and practicality of them in the biomedical field.

A semi-supervised framework fusing multiple information for knowledge graph entity alignment

Entity alignment (EA) is a fundamental task for cross linguistic knowledge graphs (KGs) understanding and interaction, which is committed to matching entities from different graphs based on their inherent semantics. Methods based on graph neural networks (GNNs) dominate the EA task, however, the majority of them ignore the higher-order information among entities in the KGs. Meanwhile, as important auxiliary information, the relational semantics, string information of entity names and attribute information of entities are insufficiently exploited during the inference phase. In addition, labeled alignment data is universally insufficient across various datasets, which limits the performance of the model. In this paper, we propose a Semi-supervised EA framework that Comprehensively considers both Structural and Attribute information within KGs (SCSA) to address these problems above. Specifically, our approach first leverages hypergraph neural networks (HGNN) to aggregate relational semantic information and graph convolutional networks (GCNs) with a highway filtering strategy to acquire the embedding representation of entities precisely. Then, we propose a bidirectional filtering technique with a combination of entity, attribute and string values to create pseudo-labeled data and lead the model for iteratively training. We implement our proposed framework on several publicly recognized cross-lingual datasets. The experimental results indicate that our framework outperforms almost all state-of-the-art (SOTA) methods.

TLF: Triple learning framework for intracranial aneurysms segmentation from unreliable labeled CTA scans

Intracranial aneurysm (IA) is a prevalent disease that poses a significant threat to human health. The use of computed tomography angiography (CTA) as a diagnostic tool for IAs remains time-consuming and challenging. Deep neural networks (DNNs) have made significant advancements in the field of medical image segmentation. Nevertheless, training large-scale DNNs demands substantial quantities of high-quality labeled data, making the annotation of numerous brain CTA scans a challenging endeavor. To address these challenges and effectively develop a robust IAs segmentation model from a large amount of unlabeled training data, we propose a triple learning framework (TLF). The framework primarily consists of three learning paradigms: pseudo-supervised learning, contrastive learning, and confident learning. This paper introduces an enhanced mean teacher model and voxel-selective strategy to conduct pseudo-supervised learning on unreliable labeled training data. Concurrently, we construct the positive and negative training pairs within the high-level semantic feature space to improve the overall learning efficiency of the TLF through contrastive learning. In addition, a multi-scale confident learning is proposed to correct unreliable labels, which enables the acquisition of broader local structural information instead of relying on individual voxels. To evaluate the effectiveness of our method, we conducted extensive experiments on a self-built database of hundreds of cases of brain CTA scans with IAs. Experimental results demonstrate that our method can effectively learn a robust CTA scan-based IAs segmentation model using unreliable labeled data, outperforming state-of-the-art methods in terms of segmentation accuracy. Codes are released at https://github.com/XueShuangqian/TLF.

Temporal Graph Network for continuous-time dynamic event sequence

Continuous-Time Dynamic Graph (CTDG) methods have shown their superior ability in learning representations for dynamic graph-structured data, the methods split the sequential updating process into discrete batches to reduce the computation costs, as a result, the message constructor in existing CTDG methods cannot be optimized by gradient descent and is designed to be parameter-free. In particular, this layer fails to embed complex event subgraphs and ignores the structure information, while most real-world events are structured and complex. For example, a paper publication event in an academic graph contains different relations like authorship and citations. Furthermore, the corresponding nodes could not receive position-wise messages to make precise representation updates. To tackle this issue, we propose a new method called Temporal Graph Network for continuous-time dynamic Event sequence (TGNE) with a structure-aware message constructor to update node representation with complex event subgraph, by treating message construction and delivery as a message-passing process, in this way, the message constructor can be formalized as a graph neural network layer. TGNE extends the input of CTDG methods to subgraphs with complex structures and preserves more information in message delivery. Extensive experiments demonstrate that the proposed method can achieve competitive performance on traditional tasks on bipartite graphs and event sequence learning tasks on heterogeneous graphs.

Phase quantification analysis in cement-blended slag system with the PONKCS method

The Partial Or No Known Crystal Structure (PONKCS) method, an XRD analysis technique, is widely employed for phase quantification in cement-blended supplementary cementitious materials (SCMs). This study emphasized unsolved factors in applying this method to SCMs blends, including obtaining accurate SCM structure information, accounting for chemical composition variation, and identifying suitable C–S–H profiles. Ground granulated blast furnace slag was chosen as the representative SCM. Results show that four fundamental parameter (FP) type peaks were sufficient to index slag pseudo-structure without Pawley fitting. Slag weight fraction quantification was minimally impacted by slag pseudo-structures from slag with SiO2 content variations up to 3.03%. The C–S–H profile, with negligible clinker residue and a Lorentzian crystallite size of approximately 6.9 nm, was found suitable for PONKCS method application. Combining selective dissolution method with PONKCS method improved its accuracy. This study enhances improving quantification precision and extending the applications of PONKCS method in variable SCMs systems.

Purity skeleton dynamic hypergraph neural network

Recently, in the field of Hypergraph Neural Networks (HGNNs), the effectiveness of dynamic hypergraph construction has been validated, which aims to reduce structural noise within the hypergraph through embeddings. However, the existing dynamic construction methods fail to notice the reduction of information contained in the hypergraphs during dynamic updates. This limitation undermines the quality of hypergraphs. Moreover, dynamic hypergraphs are constructed from graphs. Several key nodes play a crucial role in graph, but they are overlooked in hypergraphs. In this paper, we propose a Purity Skeleton Dynamic Hypergraph Neural Network (PS-DHGNN) to address the above issues. Firstly, we leverage purity skeleton method to dynamically construct hypergraphs via the fusion embeddings of features and topology simultaneously. This method effectively reduces structural noise and prevents the loss of information. Secondly, we employ an incremental training strategy, which implements a batch training strategy based on the importance of nodes. The key nodes, as the skeleton of hypergraph, are still highly valued. In addition, we utilize a novel loss function for learning structure information between hypergraph and graph. We conduct extensive experiments on node classification and clustering tasks, which demonstrate that our PS-DHGNN outperforms state-of-the-art methods. Note on real-world traffic flow datasets, PS-DHGNN demonstrates excellent performance, which is highly meaningful in practice.