Search Results (150)

The design of materials with programmable degradation profiles presents a fundamental challenge in pattern recognition across molecular space, requiring the identification of complex structure–property relationships within an exponentially large chemical domain. This paper introduces a novel physics-informed deep learning framework that integrates multi-scale molecular sensing data with reinforcement learning algorithms to enable intelligent characterization and prediction of polymer degradation dynamics. Our method combines three key innovations: (1) a dual-channel sensing architecture that fuses spectroscopic signatures from Graph Isomorphism Networks with temporal degradation patterns captured by transformer-based models, enabling comprehensive molecular state detection across multiple scales; (2) a physics-constrained policy network that ensures sensor measurements adhere to thermodynamic principles while optimizing the exploration of degradation pathways; and (3) a hierarchical signal processing system that balances multiple sensing modalities through adaptive weighting schemes learned from experimental feedback. The framework employs curriculum-based training that progressively increases molecular complexity, enabling robust detection of degradation markers linking polymer architectures to enzymatic breakdown kinetics. Experimental validation through automated synthesis and in situ characterization of 847 novel polymers demonstrates the framework’s sensing capabilities, achieving a 73.2% synthesis success rate and identifying 42 structures with precisely monitored degradation profiles spanning 6 to 24 months. Learned molecular patterns reveal previously undetected correlations between specific spectroscopic signatures and degradation susceptibility, validated through accelerated aging studies with continuous sensor monitoring. Our results establish that physics-informed constraints significantly improve both the validity (94.7%) and diversity (0.82 Tanimoto distance) of generated molecular structures compared with unconstrained baselines. This work advances the convergence of intelligent sensing technologies and materials science, demonstrating how physics-informed machine learning can enhance real-time monitoring capabilities for next-generation sustainable materials. Full article

Markerless motion capture (MMC) offers a non-invasive method for monitoring external load in sports where wearable devices are restricted; however, its validity in 3x3 basketball contexts remains unverified. The viability and measurement precision of a multi-camera RTMPose-based MMC system for single-player tasks in 3x3 basketball performance monitoring were evaluated in this study. Recorded on a standard half-court, eight cameras (60 fps) captured ten collegiate athletes executing basketball-specific activities including linear sprints, curved runs, T-tests, and vertical jumps. The 3D coordinates of hip and ankle keypoints were reconstructed from multiple synchronized camera views using Direct Linear Transformation (DLT), from which horizontal displacement and average speed were derived. These values were validated using tape-measure distance and time–motion analysis. The MMC system demonstrated high accuracy, with coefficients of variation (CVs) below 5%, mean bias under 3.5%, and standard error of estimate (SEE) below 3% across most tasks. Speed estimates revealed great consistency with time–motion analysis (ICC = 0.97–1.00; standardized change in mean [SCM] varied from trivial to small). The Bland–Altman graphs verified no proportional error and little bias. These results confirm the MMC system as a consistent, non-invasive method for gathering movement data in outdoor basketball environments. Future studies should assess the system’s performance during live competitive play with several athletes and occlusions and compare it to a laboratory-grade motion capture system. Full article

This study introduces a digital twin paradigm that uses both stationary and mobile sensors and cutting-edge machine learning for urban air quality monitoring. By boosting $R^2$ values from 0.29 to 0.87–0.95, our two-step calibration method increased the accuracy of low-cost PM sensors, showing the possibility of growing monitoring networks without sacrificing measurement accuracy. Significant temporal and spatial variability in PM concentrations was found by mobile sensor deployments, with variations of up to 300% over short distances, predominantly during heavy traffic. During rush hours, peak concentrations were found on multi-lane boulevards and intersections, indicating important exposure concerns usually overlooked by stationary monitoring networks. According to our Graph Neural Network model, which successfully described pollutant dispersion patterns, road dust resuspension predominates in residential areas, while vehicle emissions account for 65% of PM_2.5 along high-traffic corridors. Urban green areas lower PM levels by 30%, yet when the current low-emission zones were first implemented, they had no discernible effect on air quality. Municipal authorities can use this digital twin strategy to acquire practical insights for focused air quality improvements. The method helps make evidence-based traffic management and urban planning judgments by identifying unidentified pollution hotspots and source contributions. The technique offers a scalable option for establishing healthier urban development and marks a substantial leap in environmental monitoring. Full article

A lack of public vehicular traffic data for a city limits our understanding of the traffic occurring in the street networks of that city; however, there are free tools to extract street network graphs from digital maps and to assess the static properties associated with those graphs. This study proposes a two-stage modeling method to describe dynamic traffic data with static street network features. A quadratic polynomial is used to fit the average travel speed (ATS) pattern observed in the city center. Then, the relationship between the polynomial parameters and street network variables is analyzed through multiple linear regression. Descriptive geometric and topological measurements of downtown areas are obtained with the OSMnx tool (from OpenStreetMap), and with these data, independent variables are defined. The speed of vehicles, assessed every 15 min (from 6:00 a.m. to 10:00 p.m.) on the downtown street networks of twelve major cities, is obtained with the distance_matrix service of GoogleMaps, and with these data, the ATS (the dependent variable) is calculated. The ATS (presenting a U-shape) is modeled with a polynomial equation of order two, so there are three parameters for each city; in turn, each parameter is modeled with a multiple linear regression equation with the independent variables. For training purposes, the ATS equation parameters of ten cities are calculated, and the parameters, in turn, are explained with the proposed method. For validation purposes, the parameters of two cities not considered in the training process are calculated with the multiple linear regression equations. The ATS equation parameters of the twelve cities are correctly modeled so that each city’s ATS can be adequately described. It was concluded that the method selects the independent variables that are suitable to explain the ATS equation parameters. In addition, with the Akaike information criterion, the variable selection case presenting the best trade-off between accuracy and complexity is identified. Full article

In the context of an ever-evolving global economy, ASEAN companies face dynamic systemic risk that reshapes their financial interrelationships. This study examines the transmission of these risks using advanced graph theory techniques, particularly the measurement of eigenvector centrality based on Euclidean distances, combined with a hidden model that incorporates macroeconomic variables, such as GDP. The research focuses on identifying critical nodes within the corporate network, evaluating their contagion potential—both in terms of reinforcing resilience and amplifying vulnerabilities—and analyzing the influence of external factors on the network’s structure and behavior. The findings offer an innovative framework for managing systemic risk and provide strategic guidelines for the formulation of economic policies in emerging ASEAN markets. Full article

Wireless sensor networks (WSNs) use distributed nodes for tasks such as environmental monitoring and surveillance. The existing anomaly detection methods fail to fully capture correlations in multi-node, multi-modal time series data, limiting their effectiveness. Additionally, they struggle with small sample scenarios because they do not effectively map features to classes. To address these challenges, this paper presents an anomaly detection approach that integrates deep learning with metric learning. A framework incorporating a graph attention network (GAT) and a Transformer is developed to capture spatial and temporal features. A novel distance measurement module improves similarity learning by considering both intra-class and inter-class relationships. Joint metric-classification training improves model accuracy and generalization. Experiments conducted on public datasets demonstrate that the proposed approach achieves an F1 score of 0.89, outperforming the existing approaches by 7%. Full article

Point clouds from laser scanners have been widely used in recent research on indoor modeling methods. Currently, particularly in data-driven modeling methods, data preprocessing for dividing structural components and nonstructural components is required before modeling. In this paper, we propose an indoor modeling method without the classification of structural and nonstructural components. A pre-mesh is generated for constructing the adjacency relations of point clouds, and plane components are extracted using planar-based region growing. Then, the distance fields of each plane are calculated, and voxel data referred to as a surface confidence map are obtained. Subsequently, the inside and outside of the indoor model are classified using a graph-cut algorithm. Finally, indoor models with watertight meshes are generated via dual contouring and mesh refinement. The experimental results showed that the point-to-mesh error ranged from approximately 2 mm to 50 mm depending on the dataset. Furthermore, completeness—measured as the proportion of original point-cloud data successfully reconstructed into the mesh—approached 1.0 for single-room datasets and reached around 0.95 for certain multiroom and synthetic datasets. These results demonstrate the effectiveness of the proposed method in automatically removing non-structural components and generating clean structural meshes. Full article

In this paper, we study the fault-tolerant metric dimension in graph theory, an important measure against failures in unique vertex identification. The metric dimension of a graph is the smallest number of vertices required to uniquely identify every other vertex based on their distances from these chosen vertices. Building on existing work, we explore fault tolerance by considering the minimal number of vertices needed to ensure that all other vertices remain uniquely identifiable even if a specified number of these vertices fails. We compute the fault-tolerant metric dimension of various chemical graphs, namely fullerenes, benzene, and polyphenyl graphs. Full article

This study analyses social media networks in tourism using graphs focusing on user influence, hashtag patterns, and engagement. This study aims to reveal the structural function of core users, development of hashtags, and interaction patterns that construct tourism discourses. Using NodeXL 2024 for social network visualization and clustering analysis, this study measures centrality, modularity, and geodesic distances for influential user detection, topical dissemination, and engagement pattern identification. The results uncover bridging nodes between different communities, the proliferation of thematic hashtags related to sustainability and cultural heritage, and the role of emotional and visual storytelling in the use of engagement patterns. The theoretical implications also progress SNA application in tourism studies by illuminating aspects of how online discourses coalesce and the effect of SNA on access. In practical terms, this study indicates that destination marketers must consider leveraging key influencers, using strategic types of hashtags, and by monitoring engagement at key times to maximize effective destination marketing and to enhance crisis communication. These contributions notwithstanding, limitations involve the omission of sentiment analysis and the necessity for longitudinal data. By exploring new emerging platforms like TikTok and Instagram, researchers can begin to understand the more relevant trends of digital engagement. The present research offers a data-driven approach for facilitating the significance of integrating social media strategies with network externalities for tourism operators. Full article

Graphimages, which represent data structures through nodes and edges, present significant challenges for clustering due to their intricate topological properties. Traditional clustering algorithms, such as K-means and Density-Based Spatial Clustering of Applications with Noise (DBSCAN), often struggle to effectively capture both spatial and structural relationships within graph images. To overcome these limitations, we propose a novel two-stage clustering approach that integrates conventional clustering techniques with graph-based methodologies to enhance both accuracy and efficiency. In the first stage, a distance- or density-based clustering algorithm (e.g., K-means or DBSCAN) is applied to generate initial cluster formations. In the second stage, these clusters are refined using spectral clustering or community detection techniques to better preserve and exploit topological features. We evaluate our approach using a dataset of 8118 graph images derived from depth measurements taken at various angles. The experimental results demonstrate that our method surpasses single-method clustering approaches in terms of the silhouette score, Calinski-Harabasz index (CHI), and modularity. The silhouette score measures how similar an object is to its own cluster compared to other clusters, while the CHI, also known as the Variance Ratio Criterion, evaluates cluster quality based on the ratio of between-cluster dispersion to within-cluster dispersion. Modularity, a metric commonly used in graph-based clustering, assesses the strength of division of a network into communities. Furthermore, qualitative analysis through visualization confirms that the proposed two-stage clustering approach more effectively differentiates structural similarities within graph images. These findings underscore the potential of hybrid clustering techniques for various applications, including three-dimensional (3D) measurement analysis, medical imaging, and social network analysis. Full article

This work addresses the issue of closely spaced multi-target localization in distributed MIMO radars using bistatic range (BR) and angle of arrival (AOA) measurements. We propose a two-step method, decomposing the problem into measurement association and individual target localization. The measurement association poses a significant challenge, particularly when targets are closely spaced along with the existence of both false alarms and missed alarms. To tackle this challenge, we formulate it as a clustering problem and we propose a novel clustering algorithm. By carefully defining the distance metric and the set of neighboring estimated points (EPs), our method not only produces accurate measurement association, but also provides reliable initial values for the subsequent individual target localization. Single-target localization remains challenging due to the involved nonlinear and nonconvex optimization problems. To address this, we formulate the objective function as a form of the product of certain local functions, and we design a factor graph-based iterative message-passing algorithm. The message-passing algorithm dynamically approximates the complex local functions involved in the problem, delivering excellent performance while maintaining low complexity. Extensive simulation results demonstrate that the proposed method not only achieves highly efficient association but also outperforms state-of-the-art algorithms and exhibits superior consistency with the Cramer–Rao lower bound (CRLB). Full article

In the context of sustainable development, particularly in environmental monitoring and resource management, data from multiple heterogeneous sources are often incomplete or inconsistent. This presents a significant challenge for data-driven analysis, especially in tasks like clustering, where the goal is to extract meaningful patterns from multi-view data. Incomplete multi-view clustering (IMVC) aims to address this challenge by effectively leveraging complementary and consistent information despite the missing data. However, traditional graph-based clustering methods that rely on Euclidean distance often fail to capture the complex structures in high-dimensional incomplete data. To overcome this limitation, we propose Motif-Based Multi-Scale Bipartite Graph Fusion (MMBGF_IMC), a novel framework that combines multi-scale measurements with ensemble clustering. By integrating higher-order graph motifs, MMBGF_IMC significantly enhances the representation of inter-instance correlations. Empirical results on seven real-world datasets demonstrate that MMBGF_IMC outperforms existing methods by an average of 5–15% in clustering accuracy (ACC) and normalized mutual information (NMI), offering an effective solution for data fusion, modeling, and mining in sustainable development applications such as ecological monitoring, urban planning, and resource management. Full article

With the rapid growth of online educational resources, existing personalized course recommendation systems face challenges in multimodal feature integration and limited recommendation interpretability when dealing with complex and diverse instructional content. This paper proposes a graph-enhanced multimodal recommendation method (GEMRec), which effectively integrates text, video, and audio features through a graph attention network and differentiable pooling. Innovatively, GEMRec introduces graph edit distance into the recommendation system to measure the structural similarity between a learner’s knowledge state and course content at the knowledge graph level. Additionally, it combines SHAP (SHapley Additive exPlanations) value computation with large language models to generate reliable and personalized recommendation explanations. Experiments on the MOOCCubeX dataset demonstrate that the GEMRec model exhibits strong convergence and generalization during training. Compared with existing methods, GEMRec achieves 0.267, 0.265, and 0.297 on the Precision@10, Recall@10, and NDCG@10 metrics, respectively, significantly outperforming traditional collaborative filtering and other deep learning models. These results validate the effectiveness of multimodal feature integration and knowledge graph enhancement in improving recommendation performance. Full article

In recommendation tasks, most existing models that learn users’ preferences from user–item interactions ignore the relationships between items. Additionally, ensuring that the crossed features capture both global graph structures and local context is non-trivial, requiring innovative techniques for multi-scale representation learning. To overcome these difficulties, we develop a novel neural network, CoGraph, which uses a graph to build the relations between items. The item co-occurrence pattern assumes that certain items consistently appear in pairs in users’ viewing or consumption logs. First, to learn relationships between items, a graph whose distance is measured by Normalised Point-Wise Mutual Information (NPMI) is applied to link items for the co-occurrence pattern. Then, to learn as many useful features as possible for higher recommendation quality, a Convolutional Neural Network (CNN) and the Transformer model are used to parallelly learn local and global feature interactions. Finally, a series of comprehensive experiments were conducted on several public data sets to show the performance of our model. It provides valuable insights into the capability of our model in recommendation tasks and offers a viable pathway for the public data operation. Full article

This paper uses the multi-regional input–output model to measure China’s interprovincial embodied carbon transfer and constructs an interprovincial network; then, the temporal exponential random graph model is applied to analyze the spatial correlation characteristics and endogenous evolutionary mechanism of the network. The results show that interprovincial embodied carbon transfer relationships are increasingly close in China, but the weak symmetric accessibility between the eastern and central regions leads to less reciprocity in the embodied carbon network, and carbon emission inequality still exists. Based on the identification of networks, it is shown that the global network structures are stable, with obvious small-world characteristics and a core–periphery structure. And a structure-dependent effect and time-dependent effect also exist in the formation and evolution of the interprovincial embodied carbon transfer network in China. The popularity, multi-connectivity, and path-dependent effects among the provinces are significant, but the imperfection of interprovincial communication and the cooperation mechanism leads to the failure to form stable structures of ternary closed loops. Interprovincial embodied carbon transfer relationships tend to occur between provinces, with large differences in energy consumption structures, while geographical distance can hinder the formation of embodied carbon transfer relationships. Consequently, considering the spatial network correlation and its endogenous dynamic evolutionary mechanism, it is important to implement policies to guide coordinated carbon reduction among the provinces and to improve the fairness in embodied carbon transferring, in order to promote the fine governance of all links in the transferring process of embodied carbon. Full article