Search Results (1,625)

The goal of the Multimodal Named Entity Recognition (MNER) job is to identify and classify named entities by combining various data modalities (such as text and images) and assigning them to specified categories. The growing prevalence of multimodal social media posts has spurred heightened interest in MNER, particularly due to its pivotal role in applications ranging from intention comprehension to personalized user recommendations. In the MNER task, the inconsistency between image information and text information and the difficulty of fully utilizing the image information to complement the text information are the two main difficulties currently faced. In order to solve these problems, this study proposes a Multilevel Predictive Cross-Fusion Network (MPCFN) approach for Multimodal Named Entity Recognition. First, textual features are extracted using BERT and visual features are extracted using ResNet, then irrelevant information in the image is filtered using the Correlation Prediction Gate. Second, the hierarchy of visual features received by each Transformer block is controlled by the Dynamic Gate and aligned between image and textual features using the Cross-Fusion Module to align the image and text features. Finally, the hidden layer representation is fed into the CRF layer optimized for decoding using Flooding. Through experiments on TWITTER-2015, TWITTER-2017, and WuKong datasets, our method achieves F1 scores of 76.74%, 87.61%, and 82.35%, outperforming the existing mainstream state-of-the-art models and proving the effectiveness and superiority of our method. Full article

To handle the uncertainties brought by the increasing penetration of renewable energy sources and random loads, we design a stochastic multi-timescale distribution network reconfiguration (SMTDNR) framework to coordinate diverse scheduling resources across different timescales and develop an efficient heuristic algorithm to solve this complex NP-hard combinatorial optimization problem with high efficiency for medium- and high-voltage distribution networks. First, the SMTDNR problem, incorporating distributed renewable generators, fuel generators, energy storage systems, and controllable loads, is simplified through circular constraint linearization, Jabr relaxation, and second-order cone (SOC) relaxation techniques. Then, a one-stage multi-timescale successive branch reduction (MTSBR) algorithm is developed for distribution networks with one redundant branch, which transforms the SMTDNR problem into a stochastic multi-timescale optimal power flow (SMTOPF) problem. This is extended to a two-stage MTSBR algorithm for general networks with multiple redundant branches, which iteratively runs the proposed one-stage MTSBR algorithm. Numerical results on modified IEEE 33-bus and 123-bus distribution networks validate the superior optimality, feasibility, and computational efficiency of the proposed algorithms, particularly in scenarios of high renewable penetration and increased uncertainty, offering robust and feasible solutions where traditional methods may fail. Full article

This conceptual paper analyzes the effects of Donald Trump’s 2025 authoritarian regime on refugees, the US Refugee Admissions Program, and resettlement. The second Trump presidency resumed his first term’s attempt (2017–2021) at seizing power. This time, his regime launched a more sophisticated authoritarian plan to destroy the US. His 2025 term is consolidating power in the president to target all forms of migration to the US, including dismantling the US Refugee Admissions Program (USRAP) through executive overreach, circumventing statutory refugee procedures, violating human and civil rights, and disregarding judicial constraints. On January 20, 2025, he used Executive Order 14163, “Realigning the United States Refugee Admissions Program,” to indefinitely suspend the admission and resettlement of refugees for 90 days. Exceptions are made on a case-by-case basis, with national interest and plans for a white nationalist state driving the decision. Refugees at any phase of the vetting process will be denied entry. Simultaneously, Executive Order 14169, “Reevaluating and Realigning United States Foreign Aid,” was signed on January 20, 2025, to pause the US dissemination of foreign aid for 90 days. Resumption would depend on a review determining foreign assistance alignment with national interests. The implementation of Executive Order 14169 further dismantled the USRAP infrastructure by stripping federal agencies of personnel and budgets that support resettled refugees through a “stop work order” issued by the Department of State’s Bureau of Population, Refugees, and Migration (PRM) on January 24, 2025. Refugee resettlement agencies, non-profits, and faith-based organizations are vital to welcoming and assisting refugees as they adjust to their new lives. These critical organizations are now struggling to provide services to resettled refugees. Additionally, escalated, arbitrary, racially profiled deportations of alleged criminal undocumented immigrants have increased anxiety and fear among resettled refugee communities. Subsequently, the Trump administration’s indefinite suspension of the USRAP, effective from 2025 to 2028 and beyond, will impact refugees, their families, and the resettlement network. Truly, the survival of the USRAP depends on an administration that upholds the Constitution, democratic values, and the significance of US diplomatic global leadership, replacing this regime. Full article

This research examines the Lyapunov-based criterion for global asymptotic stability of Bidirectional Associative Memory (BAM) neural networks that have mixed-interval time-varying delays. Using a second-order reciprocally convex approach, this paper introduces a novel stability criterion for BAM neural networks with time delays. The literature has recently incorporated a few triple integral expressions in the Lyapunov–Krasovskii functional to lessen conservatism in the analysis of system stability with interval time-varying delays using a second-order reciprocally convex combination strategy. This research work establishes the negative definiteness of the Lyapunov–Krasovskii functional derivative and is formulated using Linear Matrix Inequalities (LMIs). The effectiveness of the proposed result is demonstrated through numerical examples. Full article

Heart murmurs, resulting from turbulent blood flow within the cardiac structure, represent some of the initial acoustic manifestations of potential underlying cardiovascular anomalies, such as arrhythmias. This research presents a deep learning framework aimed at the early detection of potential cardiac anomalies through the analysis of murmur patterns in phonocardiogram (PCG) signals. Our methodology employs a spectro-temporal feature fusion technique that integrates Mel spectrograms, Mel Frequency Cepstral Coefficients (MFCCs), Root Mean Square (RMS) energy, and Power Spectral Density (PSD) representations. The features are derived from segmented 5-second phonocardiogram (PCG) windows and subsequently input into a two-dimensional convolutional neural network (CNN) for the purpose of classification. In order to mitigate class imbalance and enhance generalization, We employ data augmentation techniques, including pitch moving and noise injection. The model under consideration has undergone training and evaluation utilizing a carefully selected subset of the CirCor DigiScope dataset. The experimental findings indicate a robust performance, with a classification accuracy recorded at 92.40% and a cross-entropy loss measured at 0.2242. The results indicate that an analysis of PCG signals informed by murmurs may function as an effective non-invasive method for the early screening of conditions that may include arrhythmias, particularly in clinical environments with limited resources. Full article

With the growing significance of hydrogen in the global energy transition, research on its pricing mechanisms has become increasingly crucial. Focusing on hydrogen markets predominantly supplied by electrolytic production, this study proposes a nodal marginal hydrogen price decomposition algorithm that explicitly incorporates the time-delay dynamics inherent in hydrogen transmission. A four-dimensional price formation framework is established, comprising the energy component, network loss component, congestion component, and time-delay component. To address the nonconvex optimization challenges arising in the market-clearing model, an improved second-order cone programming method is introduced. This method effectively reduces computational complexity through the reconstruction of time-coupled constraints and reformulation of the Weymouth equation. On this basis, the analytical expression of the nodal marginal hydrogen price is rigorously derived, elucidating how transmission dynamics influence each price component. Empirical studies using a modified Belgian 20-node system demonstrate that the proposed pricing mechanism dynamically adapts to load variations, with hydrogen prices exhibiting a strong correlation with electricity cost fluctuations. The results validate the efficacy and superiority of the proposed approach in hydrogen energy market applications. This study provides a theoretical foundation for designing efficient and transparent pricing mechanisms in emerging hydrogen markets. Full article

The classical fiber product in algebraic geometry provides a powerful tool for studying loci where two morphisms to a base scheme, $\varphi :X\to S$ and $\psi :Y\to S$, coincide exactly. This condition of strict equality, however, is insufficient for describing many real-world applications, such as the geometric structure of semantic spaces in modern large language models whose foundational architecture is the Transformer neural network: The token spaces of these models are fundamentally approximate, and recent work has revealed complex geometric singularities, challenging the classical manifold hypothesis. This paper develops a new framework to study and quantify the nature of approximate alignment between morphisms in the context of arithmetic geometry, using the tools of étale homotopy theory. We introduce the central object of our work, the étale mismatch torsor, which is a sheaf of torsors over the product scheme $X{\times }_{S}Y$. The structure of this sheaf serves as a rich, intrinsic, and purely algebraic object amenable to both qualitative classification and quantitative analysis of the global relationship between the two morphisms. Our main results are twofold. First, we provide a complete classification of these structures, establishing a bijection between their isomorphism classes and the first étale cohomology group $H_{ét}^1(X{\times }_{S}Y,\underline{{\pi }_1^{ét}(S)})$. Second, we construct a canonical filtration on this classifying cohomology group based on the theory of infinitesimal neighborhoods. This filtration induces a new invariant, which we term the order of mismatch, providing a hierarchical, algebraic measure for the degree of approximation between the morphisms. We apply this framework to the concrete case of generalized Howe curves over finite fields, demonstrating how both the characteristic class and its order reveal subtle arithmetic properties. Full article

Rural resilience building has gained increasing scholarly attention, yet existing literature overlooks the temporal dynamics of resilience evolution and lacks an integrative framework to explain cross-level mechanisms. This paper uses a longitudinal case study to explore how rural resilience transitions from a low-equilibrium to a high-equilibrium state and how neo-endogenous practices emerge in a weak institutional context. The study reveals three key findings. First, the village’s resilience evolved through three phases—institutional intervention, community capital activation, and resilience self-reinforcement—driven by co-evolutionary interactions between an enabling government and the rural community. This process is marked by chain effects of multidimensional community capital (e.g., cultural capital enhancing social capital) and overflow effects from resilience amplification (e.g., multi-scalar network). Second, exogenous resources and endogenous community capital are critical in the neo-endogenous model, but their synergy relies on vertical institutional interventions that foster horizontal networks and enhance communities’ resource absorption capacity. Third, the government enables resilience building by creating a support ecosystem that transitions from institutionally bundled resources to a higher-order composite space, facilitated by urban–rural interactions and community restructuring. The study makes three theoretical contributions: (1) it proposes an analytical framework integrating an enabling government, community capital, and ecosystem upgrading, thus advancing beyond the current community capital-centric paradigm; (2) it introduces a three-phase process model that unpacks spatiotemporal interactions across urban-rural interfaces, multi-scalar networks, and state-community relations, addressing the limitations of static factor-based analyses; (3) it reconceptualizes the role of government as an “enabling government” that mediates local and extra-local resource interfaces, challenging the neo-endogenous theories’ neglect of institutional agency. These insights contribute to rural resilience scholarship through a complex adaptive systems lens and offer policy implications for synergistic urban-rural revitalization. Full article

Hyperspectral remote sensing provides a powerful tool for crop nutrient monitoring and precision fertilization, yet its application is hindered by high-dimensional redundancy and inter-band collinearity. This study aimed to improve maize nitrogen estimation by constructing three types of two-dimensional full-band spectral indices—Difference Index (DI), Simple Ratio Index (SRI), and Normalized Difference Index (NDI)—combined with spectral preprocessing methods (raw spectra (RAW), first-order derivative (FD), and second-order derivative (SD)). To optimize feature selection, three strategies were evaluated: Grey Relational Analysis (GRA), Pearson Correlation Coefficient (PCC), and Variable Importance in Projection (VIP). These indices were then integrated into machine learning models, including Backpropagation Neural Network (BP), Random Forest (RF), and Support Vector Regression (SVR). Results revealed that spectral index optimization substantially enhanced model performance. NDI consistently demonstrated robustness, achieving the highest grey relational degree (0.9077) under second-derivative preprocessing and improving BP model predictions. PCC-selected features showed superior adaptability in the RF model, yielding the highest test accuracy under raw spectral input (R² = 0.769, RMSE = 0.0018). VIP proved most effective for SVR, with the optimal SD–VIP–SVR combination attaining the best predictive performance (test R² = 0.7593, RMSE = 0.0024). Compared with full-spectrum input, spectral index optimization effectively reduced collinearity and overfitting, improving both reliability and generalization. Spectral index optimization significantly improved inversion accuracy. Among the tested pipelines, RAW-PCC-RF demonstrated robust stability across datasets, while SD-VIP-SVR achieved the highest overall validation accuracy (R² = 0.7593, RMSE = 0.0024). These results highlight the complementary roles of stability and accuracy in defining the optimal pipeline for maize nitrogen inversion. This study highlights the pivotal role of spectral index optimization in hyperspectral inversion of maize nitrogen content. The proposed framework provides a reliable methodological basis for non-destructive nitrogen monitoring, with broad implications for precision agriculture and sustainable nutrient management. Full article

With the large-scale integration of single-phase distributed photovoltaic systems into distribution grids, issues such as mismatched generation and load, overvoltage, and three-phase imbalance may arise in the distribution network. A multi-objective optimization method for flexible distribution networks incorporating a four-leg soft open point (F-SOP) is proposed based on fuzzy chance constraints. First, a mathematical model for the F-SOP’s loss characteristics and power control was established based on the three-phase four-arm topology. Considering the impact of source load uncertainty on voltage regulation, a multi-objective complementary voltage regulation architecture is proposed based on fuzzy chance constraint programming. This architecture integrates F-SOP with conventional reactive power compensation devices. Next, a multi-objective collaborative optimization model for distribution networks is constructed, with network losses, overall voltage deviation, and three-phase imbalance as objective functions. The proposed model is linearized using second-order cone programming. Finally, using an improved IEEE 33-node distribution network as a case study, the effectiveness of the proposed method was analyzed and validated. The results indicate that this method can reduce network losses by 30.17%, decrease voltage deviation by 46.32%, and lower three-phase imbalance by 57.86%. This method holds significant importance for the sustainable development of distribution networks. Full article

Graft copolymers of polysaccharides with side chains of carbon-chain monomers have significant potential for a variety of practical applications. In this work, the effect of the N,N-methylenebisacrylamide (MBA) introduction stage and acrylamide concentration in microwave-assisted radical copolymerization with xanthan gum on the structure and sorption properties of the cross-linked graft copolymer was studied. It has been found that the spatial network density and average molecular weight of interstitial fragments can be controlled by varying these factors. Moderate crystallinity (<50%) and a highly developed surface of our synthesized samples were revealed using XRD and SEM. The graft copolymer exhibits the Schroeder effect; its liquid water sorption obeys Fick’s law and increases with MBA introduction at later stages and with increasing grafting degree, reaching 17.2 g/g. Studying the methylene blue sorption kinetics using pseudo-first/pseudo-second order models, a combined model and an average pseudo-order model have shown that the lower the monomer concentration in the reaction mixture and the earlier (from the onset of the reaction) the cross-linking agent is introduced, the higher the equilibrium sorption. The observed “equilibrium degree of sorption on xanthan gum vs. pseudo-order” relationship, which passes through a minimum, is explained by chemisorption and the sorbate consumption effect. An assumption is made about the prospects of using our synthesized copolymers for designing selective sorbents and ion-exchange membranes. Full article

Forecasting high-volume, univariate, and multivariate longitudinal data streams is a critical challenge in Big Data systems, especially with constrained computational resources and pronounced data variability. However, existing approaches often neglect multivariate statistical complexity (e.g., covariance, skewness, kurtosis) of multivariate time series or rely on recency-only windowing that discards informative historical fluctuation patterns, limiting robustness under strict resource budgets. This work makes two core contributions to big data forecasting. First, we establish a formal, multi-dimensional framework for quantifying “data bigness” across statistical, computational, and algorithmic complexities, providing a rigorous foundation for analyzing resource-constrained problems. Second, guided by this framework, we extend and validate the Adaptive High-Fluctuation Recursive Segmentation (AHFRS) algorithm for multivariate time series. By incorporating higher-order statistics such as covariance, skewness, and kurtosis, AHFRS improves predictive accuracy under strict computational budgets. We validate the approach in two stages. First, a real-world case study on a univariate Bitcoin time series provides a practical stress test using a Long Short-Term Memory (LSTM) network as a robust baseline. This validation reveals a significant increase in forecasting robustness, with our method reducing the Root Mean Squared Error (RMSE) by more than 76% in a challenging scenario. Second, its generalizability is established on synthetic multivariate data sets in Finance, Retail, and Healthcare using standard statistical models. Across domains, AHFRS consistently outperforms baselines; in our multivariate Finance simulation, RMSE decreases by up to 62.5% in Finance and Mean Absolute Percentage Error (MAPE) drops by more than 10 percentage points in Healthcare. These results demonstrate that the proposed framework and AHFRS advances the theoretical modeling of data complexity and the design of adaptive, resource-efficient forecasting pipelines for real-world, high-volume data ecosystems. Full article

Building clustering is a key challenge in cartographic generalization, where the goal is to group spatially related buildings into semantically coherent clusters while preserving the true distribution patterns of urban structures. Existing methods often rely on either spatial distance or building feature similarity alone, leading to clusters that sacrifice either accuracy or spatial continuity. Moreover, most deep learning-based approaches, including graph attention networks (GATs), fail to explicitly incorporate spatial distance constraints and typically restrict message passing to first-order neighborhoods, limiting their ability to capture long-range structural dependencies. To address these issues, this paper proposes LA-GATs, a multi-feature constrained and spatially adaptive building clustering network. First, a Delaunay triangulation is constructed based on nearest-neighbor distances to represent spatial topology, and a heterogeneous feature matrix is built by integrating architectural spatial features, including compactness, orientation, color, and height. Then, a spatial distance-constrained attention mechanism is designed, where attention weights are adjusted using a distance decay function to enhance local spatial correlation. A second-order neighborhood aggregation strategy is further introduced to extend message propagation and mitigate the impact of triangulation errors. Finally, spectral clustering is performed on the learned similarity matrix. Comprehensive experimental validation on real-world datasets from Xi’an and Beijing, showing that LA-GATs outperforms existing clustering methods in both compactness, silhouette coefficient and adjusted rand index, with up to about 21% improvement in residential clustering accuracy. Full article

In the context of industrial transitions and tariff frictions, financial markets are experiencing frequent defaults, emphasizing the urgency of upgrading credit scoring methodologies. A novel credit risk identification model integrating generative adversarial networks (GAN) and the minimax probability machine (MPM) is proposed. GAN generates realistic augmented samples to alleviate class imbalance in the credit score dataset, while the MPM optimizes the classification hyperplane by reformulating probability constraints into second-order cone problems via the multivariate Chebyshev inequality. Numerical experiments conducted on the South German Credit dataset, which represents individual (consumer) credit risk, demonstrate that the proposed generative adversarial network’s minimax probability machine (GAN-MPM) model achieves 76.13%, 60.93%, 71.78%, and 72.03% for accuracy, F1-score, sensitivity, and AUC, respectively, significantly outperforming support vector machines, random forests, and XGBoost. Furthermore, SHAP analysis reveals that the installment rate in percentage of disposable income, housing type, duration in month, and status of existing checking accounts are the most influential features. These findings demonstrate the effectiveness and interpretability of the GAN-MPM model, offering a more accurate and reliable tool for credit risk management. Full article

With the continuous increase in the penetration of single-phase microgrids in low-voltage distribution networks (LVDNs), the phase asymmetry of source–load distribution has made the problem of three-phase imbalance increasingly prominent. To address this issue, this paper proposes an active–reactive power coordinated optimization model for distribution network–microgrid clusters considering three-phase imbalance mitigation. The model is formulated within a master–slave game framework: in the upper level, the distribution network acts as the leader, formulating time-of-use prices for active and reactive power based on day-ahead forecast data with the objective of minimizing operating costs. These price signals guide the flexible loads and photovoltaic (PV) inverters of the lower-level microgrids to participate in mitigating three-phase imbalance. In the lower level, each microgrid responds as the follower, minimizing its own operating cost by determining internal scheduling strategies and power exchange schemes with the distribution network. Finally, the resulting leader–follower game problem is transformed into a unified constrained model through strong duality theory and formulated as a mixed-integer second-order cone programming (MISOCP) problem, which is efficiently solved using the commercial solver Gurobi. Simulation results demonstrate that the proposed model fully exploits the reactive power compensation potential of PV inverters, significantly reducing the degree of three-phase imbalance. The maximum three-phase voltage unbalance factor decreases from 3.98% to 1.43%, corresponding to an overall reduction of 25.87%. The proposed coordinated optimization model achieves three-phase imbalance mitigation by leveraging existing resources without the need for additional control equipment, thereby enhancing power quality in the distribution network while ensuring economic efficiency of system operation. Full article