Search Results (1,276)

This study constructs a global transportation carbon emission spatial correlation network via a modified gravity model and explores its evolutionary characteristics and dynamic mechanisms by integrating three-dimensional evolutionary analysis (node, overall, structural) and temporal exponential random graph model (TERGM). The main findings are as follows: (1) Global transportation carbon emission spatial correlation intensity keeps rising, with improved connectivity and integration, forming three regionally agglomerated correlation poles centered on the United States (America), China (Asia) and major European countries (Europe). (2) Network centrality distributes asymmetrically: Switzerland, Norway and the United States remain core nodes, while China, Japan and other Asian economies with strong direct correlation radiation are not in the core tier. (3) Third, evolutionary dynamics stem from the synergistic interaction of multidimensional attributes. ① Economic level positively drives bidirectional connection emission and attraction; economic scale and openness curb emission but boost attraction, while tertiary industry structure inhibits both. ② Only economic level and government efficiency exert significant positive effects on absdiff, fostering network heterophilic attraction. ③ Spatial and institutional proximity in edgecov effectively facilitate connection formation. ④ Endogenous network variables present a collaborative mechanism of reciprocity and transmission, constrained by network density. ⑤ Temporal effects show early connection structure forms path dependence, resulting in low dynamic variability and overall network stability. Full article

Driven by advancements in artificial intelligence technology, pedestrian trajectory prediction is shifting from traditional machine learning methods toward autonomous decision-making frameworks based on neural networks. However, the spatiotemporal uncertainty of pedestrian movement results in low accuracy of existing prediction models. To address this issue, we propose a multi-source perception fusion system based on INS-GNSS wearable devices. By integrating high-precision inertial measurement units (IMUs) and multi-mode global navigation satellite systems (GNSS), we enhance localization and prediction accuracy. For localization, we introduce a Gait Adaptive UKF (Gait-AUKF) that identifies pedestrian gait patterns and motion states by fusing multi-sensor data. An adaptive algorithm effectively suppresses trajectory drift and improves tracking accuracy. For trajectory prediction, we propose a pedestrian trajectory prediction framework based on a multi-source fusion attention mechanism. A GRU encoder extracts pedestrian trajectory features from historical motion data. An attention mechanism assigns varying weights to trajectory features across different scales. An LSTM decoder and A* path planning algorithm constrain spatiotemporal paths to generate future pedestrian trajectories. Experimental results demonstrate that compared to UKF and AKF, the Gait-AUKF reduces eastward error by 30%, northward error by 26.27%, and vertical error by 49.08%. The complete prediction framework achieves a 68.54% reduction in average position error (APE) and a 70.42% reduction in direction error (DE) compared to LSTM and Transformer models. Ablation experiments demonstrate that the integrated Gait-AUKF algorithm and A* path planning algorithm enhance model decision performance. After incorporating these algorithms, the model’s ADE decreased by 68.49% and FDE by 71.86%. Full article

Background/Objectives: Systemic lupus erythematosus (SLE) is an autoimmune disease characterized by B-cell hyperactivation and excessive autoantibody production. Z-DNA binding protein 1 (ZBP1), an innate immune sensor involved in nucleic acid recognition and cell death signaling, has been implicated in antiviral and inflammatory responses. However, its role in B-cell dysregulation during SLE remains unclear. Methods: Integrative transcriptomic analyses were performed using public datasets (GSE61635, GSE235658, GSE136035, and GSE163497) to determine the expression pattern and biological functions of ZBP1 in SLE. Bulk RNA-seq and single-cell RNA-seq data were used to evaluate ZBP1 expression across B-cell subsets. Correlations between ZBP1 expression, disease activity, and immunological parameters were assessed. RNA-seq data following ZBP1 knockdown were analyzed to explore its potential downstream pathways and molecular networks. In addition, in vitro ZBP1 knockdown experiments were conducted to examine its effects on B-cell activation, plasma cell differentiation, and antibody production. Results: ZBP1 was significantly upregulated in peripheral blood and B cells from SLE patients and was enriched in pathways related to type I interferon signaling and cytokine-mediated immune responses. Single-cell transcriptomic profiling further revealed elevated ZBP1 expression across multiple B-cell subsets, including naïve B cells, memory B cells, age-associated B cells (ABCs), and plasma cells. Clinically, ZBP1 expression in peripheral B cells was positively correlated with CD86 mean fluorescence intensity (MFI), SLE Disease Activity Index (SLEDAI) scores, and serum IgG levels, suggesting a link between ZBP1 and B-cell activation. RNA-seq analysis following ZBP1 silencing demonstrated that ZBP1 regulates genes involved in the cell cycle, DNA replication, and p53 signaling, indicating its potential role in promoting B-cell proliferation and activation. Functionally, ZBP1 silencing impaired B-cell activation, reduced plasma cell differentiation, and decreased immunoglobulin production in vitro. Conclusions: Our study identifies ZBP1 as a molecule upregulated in SLE B cells and associated with B-cell activation and disease activity. Although direct causality remains to be established, the data indicate that ZBP1 may contribute to SLE pathogenesis by modulating cell cycle-related pathways and promoting aberrant B-cell responses, highlighting its potential as a biomarker and a candidate therapeutic target in SLE. Full article

We propose a novel Expert-Driven Conditional Auxiliary Classifier Generative Adversarial Network (AC-GAN) framework tailored for heterogeneous multi-modal federated learning at edge AI devices such as the NVIDIA Jetson Orin Nano. Unlike prior works that assume idealized distributions or rely on centralized data, our approach jointly addresses statistical non-IID data, model heterogeneity, privacy protection, and resource constraints through an expert-guided training pipeline and hierarchical model updates. Specifically, we introduce a collaborative synthesis and aggregation mechanism where local experts guide conditional data generation, enabling realistic data augmentation on resource-constrained edge nodes and enhancing global model generalization without sharing raw data. Through hierarchical updates between client and server levels, our method mitigates bias from skewed local distributions and significantly reduces communication overhead compared to classical federated averaging baselines. We demonstrate that while “perfect precision” is theoretically unattainable under non-IID and real-world conditions, our framework achieves substantially improved precision and false positive trade-offs (e.g., precision 0.89) relative to benchmarks, validating robustness in practical multi-modal settings. Extensive experiments across synthetic and real datasets show that the proposed AC-GAN approach consistently outperforms federated baselines in accuracy, convergence stability, and privacy preservation. Our results suggest that expert-guided conditional generative modeling is a promising direction for scalable, privacy-aware edge intelligence. Full article

Interchange merging areas are critical safety hotspots in urban road networks, where complex vehicle interactions challenge traffic safety and efficiency. Improving safety performance at these locations is essential for developing sustainable, resilient, and intelligent urban transportation systems. To overcome the limitations of single-driver simulators, this study developed a multi-driver simulation platform based on Unity3D (Version 2022.3.1f1c1), enabling real-time interaction among multiple human drivers. High-resolution trajectory data were collected from 231 valid interaction events. An eight-direction relative position model was employed to classify behaviors into four patterns: longitudinal, lateral, front cut-in, and rear cut-in. Risk was quantified using time-exposed and time-integrated Anticipated Collision Time metrics, with events subsequently clustered into low (n = 138), medium (n = 67), and high-risk (n = 26) categories. An ordered logit regression model identified key risk factors. The results quantitatively demonstrate that interaction risk escalates significantly with abrupt speed changes (OR = 16.22) and late-stage occurrence of speed extremes (OR = 6.76) in the interacting vehicle, as well as large initial speed differences (OR = 2.45). Conversely, stable speed regulation and adaptive acceleration by the subject vehicle proved to be potent mitigating factors. These findings provide actionable insights for the development of intelligent collision warning systems and the sustainable design of interchange infrastructure. Full article

We developed an indoor positioning system combining Bluetooth direction-finding antennas with machine learning to improve localization accuracy and stability cost-effectively. It integrates existing indoor positioning and lighting control with a Bluetooth angle of arrival (AoA)-dongle, compatible with current mesh networks, using the message queuing telemetry transport protocol for data transmission to a server. The system, developed with nRF5340 and u-blox AoA antenna boards, was evaluated in an experimental field with 12 positioning points arranged in a grid. Datasets categorized by AoA antenna quantity and data preprocessing were used to train K-nearest neighbors, support vector machine (SVM), random forest, and multilayer perceptron models. Optimal parameters were identified using grid search, and models were validated using confusion matrices and F1-scores. Results indicated significant accuracy improvements of 11.11–30.51% without preprocessing and 1.17–6.32% with preprocessing when incorporating AoA features. Real-time tests revealed SVM as the best-performing model, achieving up to 96.58% accuracy, significantly enhancing positioning stability. The results of this study underscore Bluetooth direction-finding combined with machine learning as a promising solution for the Internet of Things applications. Full article

Intrusion Detection Systems (IDSs) have evolved to safeguard networks and systems from cyber attacks. Anomaly-based Intrusion Detection Systems (A-IDS) have been commonly employed to detect known and unknown anomalies. However, conventional anomaly detection approaches encounter substantial challenges when dealing with large-scale and heterogeneous data sources. These challenges include high False Positive Rates (FPRs), imbalanced data behavior, complex data handling, resource constraints, limited interpretability, and difficulties with encrypted networks. This survey reviews 60 technical papers (2019–2025) on graph-based anomaly detection (GBAD) approaches, highlighting their ability to address these challenges by utilizing the inherent structure of graphs to capture and analyze network connectivity patterns. Our analysis reveals that 32 studies (53%) employ two-stage methods while 28 (47%) use end-to-end approaches. Among the end-to-end methods, GNN-based techniques dominate, accounting for 18 of the 28 papers. We present a phased graph-based anomaly detection methodology for intrusion detection. This includes phases of data capturing, graph construction, graph pre-processing, anomaly detection, and post-detection analysis. Furthermore, we examine the evaluation methods and datasets employed in GBAD research and provide an analysis of the types of attacks identified by these methods. The most utilized datasets include CICIDS, UNSW-NB15, and DARPA, while precision, recall, and F1-score are employed in over 85% of studies. Lastly, we outline the key challenges and future directions that require significant research efforts in this area, and we offer some recommendations to address them. Full article

This study investigates how online discussion forums in an undergraduate Social Responsibility course support students’ SDG-oriented idea generation and collaborative knowledge construction. It also examines how participation roles, behavioral intensity, interaction-network influence, and goal-aligned discourse shape idea visibility and discussion. Using a mixed-methods learning analytics design, we analyzed forum logs and message texts across five SDG-linked themes (SDGs 6, 7, 12, 14, 15) by classifying contributor types, computing a Behavioral Participation Index (BPI), constructing a directed reply network and estimating PageRank centrality, extracting solution proposals, scoring semantic goal alignment, modelling weekly temporal dynamics, and fitting multivariate regressions predicting visibility (reads) and engagement (replies) while controlling for theme, message level, time, PageRank, and BPI. Results show role-differentiated participation (N = 514), meaningful cross-theme solution proposals that varied across academic groups, and peak-driven weekly activity. PageRank centrality emerged as the strongest and most consistent predictor of both visibility and engagement, whereas goal alignment showed weaker direct effects after controls, suggesting that SDG-aligned ideas do not necessarily diffuse without structural embeddedness. Among highly goal-aligned posts, specific communicative features differentiated which proposals attracted attention and interaction. These findings suggest that SDG forum design benefits from structured interaction pathways and scaffolded discourse strategies to support equitable diffusion and productive sustainability dialogue. The study does not evaluate the normative quality of sustainability positions but examines how interaction structures and discourse features shape the visibility and diffusion of student-generated ideas. Full article

Synthetic Aperture Radar (SAR) is a powerful observation system capable of delivering high-resolution imagery under variable sea conditions to support target detection and tracking, such as for ships. However, conventional optical target detection models are typically engineered for complex optical imagery, leading to limitations in accuracy and high computational resource consumption when directly applied to SAR imagery. To address this, this paper proposes a lightweight shape-aware and direction-weighted algorithm for SAR ship detection, SADW-Det. First, a lightweight streamlined backbone network, LSFP-NET, is redesigned based on the YOLOX architecture. This achieves reduced parameter counts and computational burden by incorporating depthwise separable convolutions and factorized convolutions. Concurrently, a parallel fusion module is designed, leveraging multiple small-kernel depthwise separable convolutions to extract features in parallel. This approach maintains accuracy while achieving lightweight processing. Furthermore, addressing the differences between SAR imagery and other imaging modalities, a direction-weighted attention was devised. This enhances model performance with minimal computational overhead by incorporating positional information while preserving channel data. Experimental results demonstrate superior detection accuracy compared to existing methods on three representative SAR datasets, SSDD, HRSID and DSSDD, while achieving reduced parameter counts and computational complexity, indicating strong application potential and laying the foundation for cross-modal applications. Full article

COVID, or post-acute sequelae of SARS-CoV-2 infection (PASC), is clinically defined by persistent symptoms that endure beyond acute infection and affect multiple organ systems, including the immune, cardiopulmonary, neurological, and metabolic axes. The underlying mechanisms remain poorly resolved, limiting the development of targeted diagnostics and therapeutics. MicroRNAs (miRNAs), as key post-transcriptional regulators of gene expression, control inflammatory networks, antiviral responses, mitochondrial bioenergetics, and fibrotic pathways, all of which are implicated in long COVID pathogenesis. Recent studies show durable changes in circulating miRNA signatures months after recovery from the acute phase, suggesting a role in maintaining chronic immune activation and metabolic dysfunction. Importantly, circulating miRNAs are stable, quantifiable in biofluids, and reflect systems-level dysregulation, positioning them as promising biomarker candidates for patient stratification, symptom clustering, and disease monitoring. Moreover, miRNA-directed interventions, such as mimics and antagomiRs, represent an emerging precision-medicine strategy to correct sustained molecular disturbances. This review summarizes current evidence linking miRNAs to long COVID, highlights their biomarker potential, and discusses therapeutic avenues that may help advance mechanism-based interventions for this globally emerging chronic condition. Full article

Seed germination rate is a key early trait that strongly influences rice yield. Although germination is known to be regulated by classical phytohormones and certain metabolites, the systematic metabolic regulatory network underlying natural variation, especially the key hub metabolites with causal function, still lacks in-depth analysis. In this study, we investigated 56 rice accessions showing pronounced differences in germination performance and systematically identified metabolic pathways associated with germination rate by integrating metabolomic and proteomic analyses. Pathways involved in amino acid metabolism, energy metabolism, and glutathione metabolism were coordinately activated in Rapid Germination (RG) seeds compared with Delayed Germination (DG) seeds. Among them, glutamine was significantly enriched in the RG group. Exogenous application of glutamine selectively and significantly promoted radicle and shoot elongation in a subset of DG varieties, providing direct evidence for a positive causal role of glutamine in seed germination. The variety-specific response further suggests that germination is controlled by a complex, genotype-dependent regulatory network. Together, our results highlight a glutamine-centered metabolic program as an important basis for rapid rice seed germination and provide potential targets for improving early vigor through metabolic engineering and molecular breeding. Full article

To address the issues of insufficient hydrodynamics and water stagnation in plain river network areas, this study focuses on the typical river network of the Nanxun Campus of Zhejiang College of Water Resources and Hydropower. It aims to optimize the deployment and determine the operational parameters of a bionic hydrofoil pumping device. A 2D hydrodynamic model is built using MIKE21 to simulate flow field characteristics under various conditions, including different placement positions, with or without water-blocking measures, and combinations of flow rate, water level, and flow direction. The impacts of these conditions on system head loss and river velocity are analyzed. Results show that the optimal setup involves deploying the device near the pump house with water-blocking measures, at a flow rate of 1 m³/s, a designed water level of 2.55 m, and a counterclockwise direction. This setup maintains a river velocity of no less than 0.02 m/s, meeting daily water circulation needs. The target hydraulic parameters (flow rate of 1.0 m³/s and head of 0.084 m) are used to propose a similarity theory for hydrofoils, establish scaling relationships, and derive the minimum operational frequency of three serial bionic hydrofoil pumps at 0.268 Hz under this setup. To inhibit algal growth during special periods, the velocity is raised to 0.15 m/s, requiring an increase in frequency to 2.008 Hz. These findings offer a theoretical basis and engineering support for the application and operational parameter design of bionic hydrofoil pumping devices in complex river networks. Full article

Flavor-relevant trace volatiles and microbial communities were examined in six sauce-aroma high-temperature Daqu samples. Headspace solid-phase microextraction coupled with gas chromatography–mass spectrometry (HS-SPME-GC-MS) quantified 210 trace volatile compounds across 14 chemical classes. Orthogonal partial least squares discriminant analysis (OPLS-DA) with variable importance in projection (VIP) screening was integrated with sensory scoring, correlation analysis, and molecular docking to an olfactory receptor model. Volatile profiles showed clear stratification in total abundance. Pyrazines dominated the high-total group. Tetramethylpyrazine served as a major driver. Sensory evaluation indicated that aroma explained overall quality best. (E)-2-pentenal and dimethyl trisulfide showed significant positive associations with aroma and overall scores. In the olfactory receptor, the polar residue module that provides directional constraints for Daqu odor activation was formed by Ser75, Ser92, Ser152, Ser258, Thr74, Thr76, Thr98, Thr200, Gln99, and Glu94. The hydrogen-bond or charge network was further reinforced by Arg150, Arg262, Asn194, His180, His261, Asp182, and Gln181. The core discriminant set comprised acetic acid, hexanoic acid, (E)-2-pentenal, nonanal, decanal, dimethyl trisulfide, trans-3-methyl-2-n-propylthiophane, 2-hexanone oxime, ethyl linoleate, propylene glycol, 2-ethenyl-6-methylpyrazine, 4-methylquinazoline, 5-methyl-2-phenyl-2-hexenal, and 1,2,3,4-tetramethoxybenzene. Sequencing revealed higher bacterial diversity than fungal. Bacillus and Kroppenstedtia were dominant bacterial genera. Aspergillus, Paecilomyces, Monascus, and Penicillium were major fungal genera. Correlation patterns suggested that Bacillus and Monascus were positively linked to acetic acid and 1,2,3,4-tetramethoxybenzene. Together, these results connected chemical fingerprints, sensory performance, receptor-level plausibility, and microbial ecology. Concrete targets are provided for quality control of high-temperature Daqu. Full article

Airport aprons are complex, multi-node operational hubs frequently affected by queue congestion resulting from control handovers, taxi conflicts, and external factors. To enable proactive congestion management, we propose a new and accurate method for apron queue length prediction. The core of our approach is a multi-queue network model in which queues are systematically divided by control position and taxi direction. This framework, which applies the Fluid Flow Approximation and is calibrated with historical data, effectively captures the dynamics of multi-node traffic flow. In a validation case study at Beijing Daxing International Airport (ZBAD), the model achieved high accuracy, with the mean absolute error of queue length prediction averaging 0.5 aircraft. The results demonstrate the model’s ability to characterize queue dynamics on a minute-level scale across a full day. Full article

The proliferation of massive antenna arrays and the consequent intensification of near-field effects with 6G necessitate addressing critical security challenges in near-field communication environments. This paper presents a novel artificial noise-aided spatial and directional modulation (SDMN-AN) framework, specifically tailored for secure near-field communications. The proposed system integrates legitimate receiver indices, modulation symbols, and artificial noise (AN) confined to the null space of legitimate channels, thereby enhancing both spectral efficiency and communication security. Two precoding strategies—maximum-ratio transmission (MRT) and zero-forcing (ZF)—are investigated, offering trade-offs between hardware complexity and detection overhead. Analytical derivations of bit error rate (BER) bounds, corroborated by simulation results, underscore the superiority of the SDMN-AN framework in mitigating eavesdropping threats while significantly improving spectral efficiency, positioning it as a compelling solution for next-generation secure wireless networks. Full article