Search Results (197)

Misinformation propagates more rapidly than factual content on social media, presenting significant challenges for automated misinformation detection. Existing approaches often focus solely on textual features without incorporating temporal information, treat timing and propagation as separate factors, or apply quantum-inspired methods primarily to multimodal data rather than text-centric misinformation. This study introduces QuST-TF (Quantum-inspired Semantic encoding and Temporal Transformer Fusion), a unified model designed to detect misinformation in tweets and news articles. QuST-TF integrates quantum-inspired (classical approximation) amplitude encoding, time-aware Transformer fusion, and propagation graph attention based on engagement data, without reliance on images, audio, or quantum hardware. Performance gains are achieved through quantum-inspired (classical approximation) nonlinear angular modulation (cosine and sine rotations) implemented via classical computation, rather than genuine quantum computing. All computations utilize classical Dense layers, Rectified Linear Unit (ReLU) activations, and cosine/sine functions on CPUs or GPUs; quantum hardware is not required. The quantum-inspired (classical approximation) layer applies classical rotation-based transformations to enrich the semantic representation of BERT (Bidirectional Encoder Representations and Transformer) embeddings. Temporal information is captured by a dual-attention Transformer encoder, while propagation graph attention monitors the spread of claims. Evaluation on FakeNewsNet and PHEME datasets demonstrates 91.4% and 95.5% accuracy, respectively, with 34% fewer trainable parameters compared to standard Transformers. Ablation studies indicate that quantum encoding is the most influential component (+3.0% versus without quantum encoding), surpassing the contributions of graph attention (+2.6%) and temporal attention (+2.2%). The integration of all three components yields a 1.3% synergistic improvement, confirming effective inter-module collaboration. Attention visualization enhances interpretability, supporting the utility of QuST-TF for fact-checking applications. Full article

Multimedia recommenders can use behavioral records together with visual and textual item information, but unreliable interactions and sparse histories still make user preference modeling difficult. Most graph-based methods propagate messages over observed user–item edges as if all interactions were equally informative, so incidental or semantically inconsistent behaviors may distort the learned representations. The standard recommendation loss also provides limited context for modeling dependencies within a user’s historical sequence. We propose MGDSL, a MGDSL applies a multimodal-aware topology denoising module to calculate edge reliability weights for historical interactions from collaborative, textual, and visual evidence, and uses these weights for reliability-aware historical aggregation. In parallel, a masked self-supervised auxiliary task reconstructs masked items from sequence context, adding supervision for latent preference learning. Experiments on three benchmark datasets show that MGDSL consistently improves recommendation accuracy over competitive baselines, with particularly clear gains on the sparsest dataset. Full article

Conventional recommender systems often rely on shallow collaborative signals, which limits their performance under sparse and popularity-skewed conditions. To address this, we propose a knowledge-aware framework that combines an item hypergraph induced by user interaction histories, a top-k user similarity graph, and one-hop, relation-aware knowledge-graph aggregation. The hypergraph branch learns high-order item co-occurrence representations, which are aggregated into initial user vectors and then refined through user similarity propagation. On the item side, user-conditioned relation attention aggregates one-hop KG neighbors to produce semantic item representations. User and item representations are fused by an MLP scorer, and a lightweight popularity-aware post-scoring adjustment can optionally be applied to moderate head-item dominance. Experiments on MovieLens-1M, Last.FM and Book-Crossing show strong performance among the compared baselines in AUC, ACC, and Recall@K. Full article

In complex socio-technical systems, human–AI collaboration is becoming fundamental to the processes of knowledge creation, content generation, and innovation. The existing innovation models typically consider only a single actor, the sole AI system, or a content artifact, and therefore do not capture the dynamics between these heterogeneous actors. This study introduces a Multimodal Graph Neural Network (MM-GNN), for modeling and analyzing innovation dynamics within Human–AI–Content (HAC) networks. The proposed framework is based on HAC networks as dynamic tripartite graphs, where human nodes, AI agent nodes, and content nodes are interconnected by edges representing interactions that evolve over time. Multimodal information, including text, image, code, and structured interaction traces, is merged by attention-based fusion, and multimodal dependency and evolution of interactions are modeled by relation-aware graph message passing and GRU-based temporal propagation. The innovation potential is realized as an upper-bounded composite score based on normalized novelty, entropy change, diffusion contribution, and human-rated creativity if available. The model is assessed as a composition of node-level classification and a regression model for innovation-level classification and estimation of continuous innovation potential. Experiments on synthetic HAC datasets and selected real-world AIGC corpora demonstrate that MM-GNN performs better than the graph learning and index-based baselines, with an average F1 score of 0.87, temporal stability ρ = 0.89, and lower regression error. The ablation and visualization analyses demonstrate that the multimodal fusion and temporal propagation are beneficial for representation quality, diffusion modeling, and interpretation. The results offer a mathematical and computational approach to the study of innovation as an emergent phenomenon of dynamic human, AI, and content interactions and lay the groundwork for additional validation on a more expansive socio-technical scale. Full article

Multimodal sensor fusion is increasingly used to improve observability for fault diagnosis and prognostics, enabling Remaining Useful Life estimation in complex mechatronic and robotic systems. Yet, real-world deployments remain vulnerable to sensor faults and data integrity issues—including bias and drift, miscalibration, dropouts, saturation, cross-talk, time desynchronization, and domain shift—which can propagate through fusion pipelines and lead to optimistic validation and poor generalization. These challenges are particularly consequential in safety- and health-adjacent applications such as collaborative robots, wearable/rehabilitation devices, and human-centric mechatronic systems where decisions based on faulty sensing may affect both reliability and user safety. This review synthesizes the state of the art on (i) sensor fault taxonomies and fault models relevant to multimodal fusion, (ii) fault-aware fusion strategies spanning data-, feature-, and decision-level integration, and (iii) how sensor faults and uncertainty impact diagnosis and remaining-life estimators. We will conduct a systematic scoping review of peer-reviewed literature, extracting sensor modalities, fault characterization or injection protocols, fusion architectures, validation settings (simulation, hardware-in-the-loop, bench, and in-field/on-body studies), and reporting completeness. Beyond summarizing methods, we provide practical reporting standards for sensor-fusion-based diagnosis and prognostics, including a minimum disclosure set covering synchronization, fault ground truth, missingness handling, leakage controls, uncertainty calibration, and task-relevant metrics. Reusable checklists and evidence tables are included to support more comparable, reproducible, and deployment-ready research. Full article

The Welded Bead Bending Test (WBBT), used in Germany to assess the crack-arrest capacity of structural steels, is conducted in accordance with ZTV-ING Part 4 or Deutsche Bahn Standard 918 002-02 and specified in Stahl-Eisen-Prüfblatt 1390. Three possible test outcomes are distinguished: passed if a bending angle of $\alpha \ge 60°$ is reached without fracture but with visible cracks in the base material, not passed if fracture occurs beforehand, and invalid if no crack propagates into the base material despite bending to $\alpha \ge 60°$. This study proposes a novel data-driven approach for predicting WBBT outcomes using a Convolutional Neural Network (CNN) applied to patch-wise classification of Light Optical Microscopy images (LOMs) taken from WBB-tested samples. A dataset comprising 800 LOMs from 40 steel samples originating from various manufacturers was acquired in collaboration with Chemnitzer Werkstoff- und Oberflächentechnik GmbH. Five CNN architectures are evaluated in terms of Accuracy, Recall and Specificity: MicroNet-pretrained DenseNet-121 and EfficientNet-B0, ResNet-34 pretrained on both ImageNet (I-ResNet-34) and MicroNet (M-ResNet-34), and a light CNN trained from scratch. The models were subjected to training in accordance with three different methods, which varied by patch size and number of LOMs utilised for training. Two validation strategies, patch-level and sample-level splitting, were employed to analyse potential data leakage effects. The I-ResNet-34 model demonstrates the best performance in this comparison, achieving a patch-level Accuracy of 79.58% ± 6.82% and an image- and sample-level Specificity of 100% under sample-level splitting. This performance is confirmed via leave-one-sample-out cross-validation, yielding a comparable patch-level Accuracy of 79.36% and a Specificity of 86.26%. The corresponding WBBT sample-level results under this validation scheme are approximately 86% Accuracy and 91% Specificity. Full article

Deploying Large Language Models (LLMs) in collaborative multi-agent settings represents a promising frontier for complex AI problem-solving, yet the field lacks systematic mechanisms to manage the inherent coordination overhead and resource contention that arise at scale. Existing LLM-based Multi-Agent System (MAS) frameworks predominantly adopt sequential or loosely coupled execution models, which fail to exploit the parallelism potential of modern computing environments and limit overall system throughput. To bridge this gap, this paper presents DynTaskMAS, a framework that redefines task orchestration in LLM-based MASs through a dynamic task graph abstraction. Rather than treating tasks as static pipelines, DynTaskMAS continuously models task interdependencies at runtime, enabling opportunistic parallel execution while preserving logical correctness. The architecture integrates four synergistic components: a runtime task decomposition module that captures evolving dependencies among subtasks; a scheduling engine that dispatches ready tasks to available agents without centralized bottlenecks; a context propagation layer that maintains shared semantic state across concurrently executing agents; and a self-tuning workflow controller that adapts execution priorities based on observed system load. Together, these components address a core tension in LLM-based MAS design, balancing agent autonomy with coordinated efficiency. Evaluations across tasks of varying complexity confirm that DynTaskMAS delivers substantial gains in execution efficiency (21.3–33.0% reduction), resource utilization (from 65% to 88%), and agent scalability (3.47× throughput with 16 concurrent agents) compared to sequential baselines. This work offers a generalizable architectural blueprint for next-generation LLM-based Multi-Agent Systems operating under real-world dynamic and resource-constrained conditions. Full article

Small object detection in UAV aerial imagery poses significant challenges due to sparse pixel representation and ambiguous object boundaries. Through systematic analysis, we identify three critical degradation stages during forward propagation in deep detection networks: edge attenuation during feature extraction, semantic conflict during feature fusion, and detail loss during feature reconstruction. Existing methods address these stages in isolation or implicitly, lacking collaborative and stage-aware repair strategies. To address this issue, we propose EAGLE-DET, a novel detection framework based on sparse multi-scale attention and refined transformation. Specifically, the framework comprises three core modules: (1) the Cross-stage Multi-resolution Edge Enhancement Network (CMENet), which preserves small object edge representations via adaptive high-low frequency decomposition; (2) the Attention-guided Multi-scale Feature Fusion Network (AMFFN), which resolves cross-scale semantic conflicts through pyramidal sparse attention and multi-scale spatial decoupling; (3) the Enhanced Upsampling with Channel Bridging and Spatial Coordination module (EUCBSC), which recovers spatial detail fidelity via bidirectional channel shift mixing. Extensive experiments on three benchmark datasets—VisDrone-2019, UAVDT, and DOTA1.0—demonstrate the effectiveness of EAGLE-DET, which achieves improvements of 4.5% AP50 and 2.9% AP50:95 on VisDrone-2019 over the baseline, while maintaining inference at 71.7 FPS, achieving an optimal accuracy–efficiency trade-off. Full article

With the widespread adoption of Wi-Fi 7 in campus networks, high-density access and large-scale research data transmission challenge traditional congestion control algorithms. TCP-bottleneck bandwidth and round-trip propagation time (BBR) lacks deep link awareness and service semantic breadth, leading to misinterpreting non-congestive packet loss and inter-flow unfairness in complex wireless scenarios. To address this, this paper proposes semantic-aware and cross-layer collaborative optimized BBR (SAC-BBR), a semantic-aware cross-layer optimization mechanism for high-density heterogeneous campus networks. It leverages an Extended Berkeley Packet Filter (eBPF) to capture physical link characteristics in real time within the Linux kernel, accurately distinguishing random loss from congestion loss. It then constructs a lightweight semantic identification engine to classify traffic and establish a service satisfaction utility model. Finally, a deep reinforcement learning-based dynamic gain regulator maps cross-layer states and service priorities to the action space, enabling millisecond-level intelligent tuning of pacing_gain and cwnd_gain. Experimental results show that SAC-BBR improves throughput by over 22% compared to BBRv3 and reduces average round-trip time (RTT) by 17% while suppressing RTT jitter by over 60% in high-density scenarios. Furthermore, it enhances the Jain fairness index to 0.93 under multi-protocol competition, ensuring high-performance and equitable transmission. Full article

Digital technologies make it possible to combine multiple technical functionalities within applications that address practical and organizational needs. This paper presents Cozzmo, an Android mobile prototype for supporting communication and coordination in shared households. The system combines chat, polls, chores, shopping support, photo albums, presence awareness, mood indicators, and location-based alerts in one application. The prototype was implemented in native Java for Android using Firebase services and an MVVM architecture with LiveData. Its real-time behavior was evaluated on two physical Android devices under mixed connectivity conditions, including mobile data, hotspot use, and temporary connection loss. The evaluation examined end-to-end propagation delay, recovery after reconnection, and state convergence during concurrent user actions. In the reported test sessions, the prototype preserved update order in baseline scenarios, recovered queued messages after short interruptions, and reached a consistent final state in the concurrent voting and task-update tests. The time needed for updates to appear in the interface was less than the propagation delay, suggesting that the measured response path was shaped mainly by network and backend propagation. These findings indicate that the prototype is technically viable and can serve as a basis for further work on mobile systems for household collaboration. Full article

The diagnosis of missing seedlings in high-density drill-seeded crops is often hindered by the strong coupling between visual perception and diagnostic rules, which leads to an irreversible cascade amplification of underlying missed detection errors. To address this dilemma, this paper proposes a “Perception–Topology” collaborative decoupling framework oriented toward row structure perception. In the perception phase, a row-structure-enhanced detection model (RS-YOLO) is constructed. It integrates Space-to-Depth (SPD) conversion, a Selective Frequency-domain Aggregation Module (SFAM), and a Row-Structure Attention Mechanism (RSM) to effectively suppress tire rut interference and explicitly reinforce the spatial topological priors of crops. In the diagnostic phase, an Adaptive Intra-row Gap Analysis (AIGA) algorithm is proposed. By utilizing a dynamic median intra-plant spacing scale and core canopy geometric pruning, this algorithm fundamentally reformulates missing seedling diagnosis into a physical interruption metric of one-dimensional graph connectivity. Evaluated on a finely reconstructed UAV-based sorghum imagery dataset, RS-YOLO achieved a significant improvement of 2.7% in precision and 3.2% in recall over the baseline model, providing a structure-aligned, high-confidence input for the diagnostic process. Based on this perceptual foundation, the AIGA algorithm ultimately achieved a diagnostic precision of 96.11% and a recall of 91.48% without the need for negative sample annotations. This framework effectively severs the propagation chain of perceptual errors, providing a noise-robust and highly physically interpretable new paradigm for the automated inspection of field population structures. Full article

Composite hydrogen storage vessels exhibit pronounced anisotropy, multilayered winding architectures, and strong ultrasonic attenuation, which severely degrade the focusing accuracy and defect visibility of the conventional isotropic total focusing method (TFM). To address these challenges, this study proposes an enhanced TFM framework for defect inspection in composite hydrogen storage vessels by integrating anisotropic delay correction, Gray-code coded excitation, and coherence-weighted reconstruction. First, an anisotropic propagation delay model is established using forward ray tracing to compensate for beam deviation and focusing mismatch induced by the anisotropic winding structure. Then, Gray-code excitation and pulse compression are introduced to improve signal energy and echo detectability under high-attenuation conditions. Finally, coherence-weighted imaging is applied to suppress incoherent background noise and structural artifacts, thereby enhancing defect contrast and image readability. The proposed method is validated on hydrogen storage vessel specimens containing artificial defects, with CT results used as references. Experimental results show that, compared with conventional isotropic TFM, the proposed collaborative approach significantly improves defect imaging quality for defects of different sizes and depths. The signal-to-noise ratio is increased from 7.2, 12.8, 14.8, and 7.4 dB for isotropic TFM to 32.5, 29.9, 52.6, and 42.7 dB, respectively, for the combined anisotropic, coded-excitation, and coherence-weighted TFM. In addition, the defect depth estimation remains stable and agrees well with the CT references, yielding approximately 9.0–9.6 mm for shallow defects and 18.7–19.3 mm for deeper defects. These results demonstrate that the proposed method can effectively improve defect detectability, image contrast, and depth characterization for embedded delamination-like artificial defects in composite hydrogen storage vessels, providing a promising ultrasonic imaging strategy for thick-walled anisotropic composite pressure structures. Full article

Alpine glaciers are a vital resource for mountain regions. They provide water reserves, support energy production and tourism, and promote biodiversity. However, they are highly susceptible to climate change. In fact, they are recognised as being among the areas most affected by, and increasingly exposed to, natural hazards. The Rutor glacier in Aosta Valley, Italy, which has been the subject of repeated measurements since the 19th century and currently covers an area of around 8 km², is undergoing significant and continuous retreat. It thus serves as an exemplary case study of the impact of climate change on the Italian Alps. This ongoing research has made it possible to conduct multi-temporal analysis of the glacier. Within this framework, Politecnico di Torino, in collaboration with ARPA Valle d’Aosta, has developed a multidisciplinary research approach focused on the characterisation of alpine environments. This study illustrates the geomatic workflows and derived geospatial products that can be used to carry out a 4D monitoring of the extent and volume of the Rutor Glacier and estimate its mass balance over the past six years. A specific focus of the study is the propagation of errors in multi-temporal analyses used to quantify glacier melt, with particular attention to the precision of input 3D geospatial data and to the Limit of Detection of elevation differences, ultimately enabling the estimation of the uncertainty associated with the derived quantities and their temporal trends. Finally, advantages and limitations in the multi-temporal and multi-sensor monitoring of glaciers are presented and discussed. Full article

Field water status is a critical variable for agricultural water management. In recent years, the development of space–air–ground multi-platform collaborative observation and data fusion technologies has provided new options for precision monitoring. However, challenges in applicability, robustness, and transferability persist. This study employs bibliometric analysis to systematically synthesize the literature, revealing that research has evolved from single-point observations to multi-platform synergy. Satellite, unmanned aerial vehicle (UAV), and ground-based monitoring are analyzed, as well as challenges in multi-source data fusion, including scale mismatch, error propagation, and uncertainty quantification. Finally, applicability and other barriers are evaluated across three typical agricultural scenarios: large-scale surface soil moisture monitoring, crop root zone soil moisture retrieval, and paddy field water depth estimation. The results indicate that space–air–ground collaborative observation constitutes a mature framework, with satellite and ground-based monitoring as core components and UAV technology as a supplement. However, scale transformation and error propagation mechanisms in multi-source data fusion remain unresolved. Currently available vertical water information is limited, and quantitative retrieval has yet to achieve the reliability required for operational applications. This limitation is particularly evident in paddy field water depth retrieval and root zone soil moisture retrieval. This review provides a theoretical reference for precision field water status monitoring and identifies future research priorities, including the integration of physical mechanisms with machine learning (ML) in multi-source data fusion, as well as error quantification and paddy field water depth retrieval. Full article

Accurate prediction of the Remaining Useful Life (RUL) of special gas cylinders is critical for industrial safety management. However, the nonlinear, strongly coupled degradation behaviors of these cylinders, combined with non-stationary and high-noise monitoring data, limit the performance of single deep learning models. Traditional hyperparameter tuning and signal processing methods often fail to meet the required prediction accuracy. To address these challenges, this study proposes a hybrid SSA–PSO–ResNet–LSTM–Attention framework for RUL prediction of special gas cylinders. The framework first applies Singular Spectrum Analysis (SSA) to decompose and reconstruct the 12-dimensional multi-source sensor signals, effectively suppressing noise while extracting core degradation trends. Subsequently, a ResNet–LSTM–Attention collaborative model is constructed, where ResNet ensures stable spatial feature propagation, LSTM captures long- and short-term temporal dependencies, and a multi-head attention mechanism emphasizes critical time steps associated with abrupt degradation. Furthermore, a Particle Swarm Optimization (PSO) algorithm is employed to globally optimize key hyperparameters, including the number of convolutional kernels, LSTM hidden units, and learning rate, mitigating the subjectivity of manual tuning. Experimental validation is conducted on 1000 real monitoring samples from 100 composite material gas cylinders, with a cylinder ID-based 7:1:2 train–validation–test split and stratified sampling covering four operating conditions. PSO optimizes hyperparameters using the validation set RMSE as the fitness function, and the test set is exclusively used for final performance evaluation. All results are reported as the mean ± standard deviation from grouped 5-fold cross-validation on the cylinder-wise partition. The proposed model achieves a test RMSE of 71.55, MAE of 50.63, and R² of 0.9584, representing a 34.2% and 30.2% reduction in RMSE and MAE, respectively, compared with the second-best CNN-LSTM model, and significantly outperforming SVR, MLP, and other benchmark models. Ablation studies confirm the positive synergistic effect of each component, with the removal of either the attention mechanism or the ResNet module causing substantial performance degradation. By employing physically calibrated RUL labels and a balanced multi-condition dataset, the proposed framework achieves high predictive accuracy and good potential for industrial application, providing an effective solution for RUL prediction of special gas cylinders and similar high-pressure vessels, with potential applications in intelligent maintenance of complex industrial equipment. Full article