Search Results (615)

Urban traffic congestion prediction is an important problem in smart city sensing and intelligent traffic governance. Existing methods mostly rely on single-source traffic flow sensing data or static road topology, making it difficult to sufficiently characterize the dynamic congestion propagation process driven by multisource sensing information, such as traffic flow, vehicle trajectories, road images, public transportation, meteorological conditions, and sudden events. To address this issue, a spatiotemporal causal graph learning framework based on multisource urban sensing data is proposed for urban traffic state prediction, congestion identification, and explainable early warning. In this framework, traffic flow detector data, GPS trajectories, roadside camera data, public transportation data, weather data, and event records are first fused through a multisource urban sensing data collaborative encoding module, and the influence of low-quality or missing sensing modalities is suppressed using a reliability-aware attention mechanism. Subsequently, time-varying causal propagation relationships among road segments are adaptively learned from historical traffic states, road topology, and external disturbances through a dynamic spatiotemporal causal graph learning module. Finally, spatial diffusion and temporal evolution are jointly modeled by a causality-explanation-driven congestion prediction module, and key congestion sources, propagation paths, and inducing factors are outputs. Experimental results based on multisource traffic sensing data from the main urban area of Hangzhou show that the proposed method achieves MAE values of $3.21$, $3.79$, and $4.48$ in 15-min, 30-min, and 60-min traffic state prediction tasks, respectively, outperforming ARIMA, XGBoost, LSTM, Transformer, STGCN, Graph WaveNet, GMAN, Multimodal Transformer, and the Causal Temporal Graph Network. In the ablation study, the complete model achieves an Accuracy of $0.914$, a Precision of $0.902$, a Recall of $0.889$, an F1 of $0.895$, and an AUC of $0.956$. For congestion identification and early warning under complex scenarios, F1 values of $0.927$, $0.904$, and $0.893$ are achieved under peak-hour, rainy-weather, and traffic-event scenarios, respectively; the corresponding AUC values reach $0.966$, $0.957$, and $0.948$; and the false alarm rate (FAR) values are reduced to $0.061$, $0.072$, and $0.081$. The results indicate that the proposed method can effectively improve traffic state prediction accuracy, congestion early warning reliability, and model interpretability under multisource urban sensing conditions, thereby providing an effective technical pathway for AI-driven intelligent traffic sensing. Full article

Artificial intelligence-integrated ‘buy now, pay later’ (BNPL) platforms are diffusing rapidly across the Middle East and North Africa (MENA), raising concerns about consumer financial vulnerability. Drawing on choice architecture, payment decoupling, and financial literacy literatures, this study examines how three platform-level features—algorithmic nudging, AI personalization intensity, and perceived ease of credit—are associated with impulsive buying tendency and downstream financial outcomes, and whether BNPL-specific financial literacy attenuates these associations. A multi-method design combined cross-sectional partial least squares structural equation modeling (N = 1247 active BNPL users in seven MENA countries) with a six-month longitudinal follow-up (N = 847, 68% retention). Algorithmic nudging was positively associated with impulsive buying tendency, which in turn was associated with elevated financial stress and longitudinal debt accumulation. The ‘loyalty trap’—a paradoxical state in which financially stressed consumers maintain high platform loyalty—is provisionally documented via piecewise longitudinal trajectories. We emphasize that this pattern is consistent with but not causally established by the present design, and we outline specific experimental and quasi-experimental research designs needed for causal identification. BNPL-specific financial literacy moderated the associations between algorithmic nudging, impulsive buying, and adverse financial outcomes, with the highest-literacy quartile exhibiting substantially attenuated debt trajectories. We discuss boundary conditions, alternative explanations, and the limits of causal inference in non-experimental panel data. Findings inform evolving BNPL regulatory frameworks in MENA, with particular relevance to nudge-transparency disclosures, contractual cooling-off periods, and credit-bureau reporting standards. Full article

Accurate vessel trajectory prediction using Automatic Identification System (AIS) data is crucial for maritime traffic supervision, collision risk assessment, and search and rescue operations. However, long-horizon prediction remains challenging because AIS trajectories involve complex navigation patterns, continuous motion variations, and cumulative forecasting errors. To address these issues, this study proposes MAKDformer, a Transformer-based model for vessel trajectory prediction that integrates multi-attribute discrete state modeling with kinematic differential perception. MAKDformer uses latitude, longitude, speed over ground, and course over ground as input variables, and employs a Transformer backbone to model long-range temporal dependencies. To capture continuous vessel motion dynamics, a Kinematic Differential Perception Module (KDPM) is developed to extract sequential variations in displacement, speed, acceleration, and course. These kinematic differential features are then fused with the discrete Transformer representations. Furthermore, auxiliary regression loss and motion-consistency trend loss are incorporated to regularize motion pattern learning and enhance long-horizon forecasting stability. Experiments on real-world AIS datasets demonstrate that MAKDformer achieves superior spatial prediction accuracy compared with baseline models, including the standard Transformer, TrAISformer, MART, and GeoTrackNet. Specifically, in the 15 h forecasting task, MAKDformer reduces the Haversine error by approximately 40.1% compared with the second-best model, GeoTrackNet. Ablation experiments further verify that explicitly integrating KDPM, the feature fusion mechanism, and motion-constrained losses effectively mitigates error accumulation and improves the robustness of vessel trajectory prediction. Full article

Previous research has shown sector-conditional asymmetry in implied volatility levels and in option returns. However, no prior work has parameterised that asymmetry at the effective-theta layer in a form that fires a non-discretionary rule trigger. This study supplies the parameterisation, its formulation, the first observation, and the data evidence. An effective theta is defined as ${\Theta }_e=\alpha \left(s,r\right)\cdot {\Theta }_{BS}$, where ${\Theta }_{BS}$ is the standard Black–Scholes (BS) theta and $\alpha \left(s,r\right)$ is a sector- and regime-conditional scaling factor. A SIMDEC decomposition is used to filter the input space and to determine the corner where $\alpha$ matters most. The framework is a bounded retrieval-and-deterministic compute system. The instruments are retrieved from cached market data and the learned layer’s outputs are constrained to that admissible set. Therefore, by construction, it cannot confabulate a fictitious or out-of-bounds instrument and the generative-class hallucination failure mode cannot occur. This concerns the groundedness and bounds of every output and is distinct from the accuracy of the regime and quality labels. SIMDEC supplies the joint-state filtering partition and, together with the Sobol variance decomposition, an explainability and attribution layer in which every position-level evaluation maps to an interpretable joint-state bin and a variance-share attribution. A “first observation” arising from a three-position long-call cohort traversing terminal decay is deployed using eight intraday states tracked on the trajectory at primary-source resolution and illustrates the relationship of the $\alpha$ parameterisation to existing market conditions. To examine the effectiveness of the approach, a SIMDEC dataset from the same deployment supplies population-level support across 12 sectors and a three-tier quality stratification. The dataset is the output of the THETA AI/ML pipeline—a multi-architecture deep-learning inference system that treats SIMDEC joint-state partitioning and Sobol variance decomposition as complementary interpretability inputs, with the regime classifier carrying the labels and the composite quality scorer carrying the stratification. The PC-based, token-free analytical procedure for regulated decision-making settings, together with an illustrative example of the asymmetry in the effective-theta provide a “next level” contribution to traditional option methodology. Full article

The convergence of the World Wide Web and artificial intelligence (AI) has fundamentally reconfigured digital ecosystems. Yet conventional generational models—Web 1.0 through 4.0—remain inadequate for capturing the recursive, mutually constitutive dynamics that characterize this coevolution. The historical trajectory of the Web reveals a progressive expansion of human agency: Web 1.0 afforded read access; Web 2.0 enabled user-generated content; Web 3.0 introduced digital ownership. Artificial intelligence has followed a parallel arc. Early generative systems, such as ChatGPT, demonstrated read capabilities—conditional upon human authorization. Subsequent code-generation agents, including Codex and Claude Code, extended this to write capabilities—also contingent upon human approval, initiation, and financial compensation. Web 4.0, however, constitutes a qualitative rupture: AI agents now read, write, own, earn, and transact autonomously, without requiring human oversight. Such automatons operate on their own behalf or on the behalf of a creator who may be human, another agent, or entirely absent. In the Web 4.0 paradigm, the end user is no longer human—it is AI itself. This review addresses the analytical inadequacy of existing models by introducing the CET-WAIP framework (CoEvolutionary Tiers of Web and AI Paradigms), a novel classificatory framework that models AI–Web coevolution across seven intelligence tiers spanning infrastructural complexity, cognitive capabilities, and governance dimensions. Grounded in an integrative literature review with PRISMA-informed reporting, the framework aligns key AI paradigms—from rule-based systems to agentic AI—with corresponding transformations in Web architecture, revealing how intelligence scaling reshapes user agency, data structures, and ethical oversight. To demonstrate its analytical utility, we conduct a multi-tiered analysis of ChatGPT and compare it with open-source agentic systems (AutoGPT, LangChain, Sora), showing how architectural dissonance between cognitive capabilities and infrastructure is systematically diagnosable. The findings highlight the limitations of linear Web evolution frameworks and underscore the need for intelligence-centric approaches that integrate technological, cognitive, and governance dimensions. We conclude by outlining a research agenda for hybrid intelligence, adaptive governance, and equitable human–AI collaboration in ecosystems where both human and non-human agents participate as first-class actors. Full article

Large language models (LLMs) have become central to how people access and interact with information, yet their potential to reproduce ableist bias remains underexamined, especially in non-English-language settings. This study examines disability representation in LLM-generated outputs across English and Bahasa Indonesia using a hybrid analytical framework. A total of 360 responses were generated by ChatGPT, Gemini, and Microsoft Copilot through a factorial prompt design varying disability type, socioeconomic class, and language. The proposed analysis combined lexicon-based sentiment analysis, topic modeling, qualitative thematic analysis (three analysts), and consensus-based human ableism scoring. The results show that positive sentiment dominated across both languages but did not reliably indicate non-ableist representation. Ableist classifications were most concentrated in lower socioeconomic class condition, with schizophrenia-related prompts and Gemini-generated outputs showing the highest proportions of ableist classifications across disability types and LLMs, respectively. Theme-level analysis showed that ableism was most prevalent in responses framing disability through sensory overload, mobility barriers, and uneven professional access in negative sentiment outputs, and through life trajectories, aspirations, and participation shaped by socioeconomic access in positive sentiment outputs. Overall, ableist bias appeared intersectionally across socioeconomic class and disability type, with the proposed hybrid framework providing a more sensitive approach for identifying ableist representation in AI-generated text. Full article

Recent advances in wearable, vision-based, trajectory, physiological, and multimodal sensing technologies, together with deep learning, have enabled continuous, objective, and individualized assessment of sport performance and athlete health. Unlike prior reviews that primarily focus on a single sensing modality, sport, or algorithmic series, this review integrates wearable, vision-based, trajectory, physiological, and multimodal sensing streams with deep learning models across both performance analysis and athlete health monitoring, thereby clarifying modality-task-model relationships and translational limitations. This review synthesizes recent progress in sensor-based sports intelligence, focusing on how heterogeneous data streams are transformed into performance- and health-related decision support. The reviewed applications include athlete and ball perception, multi-object tracking, pose estimation, action recognition, trajectory and tactical analysis, training-load and fatigue monitoring, injury-risk prediction, rehabilitation monitoring, and return-to-play support. Deep learning architectures, including CNNs, LSTMs, GRUs, TCNs, Transformers, attention mechanisms, graph neural networks, and multimodal fusion models, are discussed in relation to their suitability for visual, temporal, spatial, physiological, and multisource data. This review further identifies key challenges, including data heterogeneity, annotation scarcity, limited cross-sport and cross-device generalization, real-time deployment constraints, model interpretability, privacy protection, and ethical governance. Moving forward, research efforts should focus on the development of standardized datasets, reliable multimodal data fusion strategies, self-supervised and transfer learning approaches, and deployment on edge or cloud computing platforms. Additionally, enhancing interpretability through explainable AI and implementing closed-loop, individualized monitoring systems are critical. By synthesizing advances in sensing technologies, deep learning methodologies, and real-world applications, this review aims to provide a practical reference for optimizing athletic performance, preventing injuries, guiding rehabilitation, and supporting long-term health management of athletes. Full article

Cardiac surgery remains a high-risk, resource-intensive domain in which perioperative complications significantly influence clinical outcomes, institutional performance, and healthcare expenditure. Despite advances in technique and protocol standardization, contemporary perioperative management largely relies on static risk stratification and reactive quality assessment. This narrative review synthesizes the current evidence on artificial intelligence (AI) and machine learning for perioperative risk prediction in cardiac surgery, spanning acute kidney injury, mortality, prolonged mechanical ventilation, postoperative atrial fibrillation, and intensive care unit deterioration, and critically appraises the methodological limitations, validation gaps, and fairness concerns that constrain clinical translation. Across these applications, predictive models have demonstrated incremental discrimination over conventional risk scores, yet remain predominantly endpoint-specific, single-institution, and disconnected from prospective clinical implementation. Building on this evidence, we propose Preventive Cardiovascular Intelligence (PCInt) as one possible organizing framework that integrates predictive analytics, dynamic risk trajectory modeling, and structured quality improvement methodologies, and we outline how such a framework might be operationalized across the surgical lifecycle. PCInt is presented as a conceptual proposal requiring prospective validation rather than as a validated system. We conclude by discussing implementation barriers, regulatory and ethical considerations, and priorities for future research toward anticipatory, value-based perioperative cardiovascular care. Full article

Autonomous driving systems rely on a sophisticated pipeline of artificial intelligence models to perceive, predict, and plan in dynamic environments. This review presents a systematic analysis of the machine learning and deep learning models underpinning vehicle autonomy, spanning classical convolutional neural networks (CNNs) for object detection and semantic segmentation to recurrent and Transformer-based architectures for trajectory prediction and motion planning. It also provides a critical examination of the autonomous vehicle sensor stack, including cameras, LiDAR, radar, ultrasonics, and GNSS/IMU as data acquisition systems, highlighting modality-specific AI challenges such as monocular depth estimation, 3D point cloud processing, and radar Doppler interpretation. The evolution of perception and decision-making pipelines is reviewed, contrasting modular architectures with end-to-end learning paradigms that directly map raw sensor data to control commands, and discussing their trade-offs in interpretability, safety assurance, and robustness to rare edge cases. We further survey specialized hardware accelerators and heterogeneous automotive SoCs designed to meet stringent real-time and power constraints. Industrial strategies are compared, including multi-modal sensor fusion and vision-centric approaches based on large-scale imitation learning. Finally, we identify open challenges related to robustness under adverse conditions, domain shift, causal ambiguity, and the need for interpretable and certifiable AI in safety-critical autonomous driving systems. Full article

Spaceborne Synthetic Aperture Radar (SAR) provides all-weather, day and night and wide-area imaging capability, and plays a critical role in maritime surveillance. While substantial progress has been achieved in SAR ship detection, SAR ship classification remains relatively underexplored, mainly due to the scarcity of reliable category labels. Automatic Identification System (AIS) provides vessel identity, type, and dynamic trajectory information, and thus offers vessel type information that is difficult to obtain directly from SAR imagery. This paper proposes a fine-grained nine-category SAR ship classification method based on AIS association, which reorganizes the original AIS vessel types into nine fine-grained categories of SAR ship, transfers AIS vessel type information to SAR detection through a global optimal matching strategy, and supports SAR-only vessel category recognition. By retaining only high-confidence SAR and AIS matched pairs and cropping the corresponding SAR ship chips, an SAR ship classification dataset containing 4472 ship chips across the nine categories is constructed. In Monte Carlo experiments based on real AIS records, the proposed association strategy achieves more reliable high-confidence label generation than the compared association methods under close ship ambiguity, spatial perturbation, distractor AIS candidates, and AIS static size errors. In the benchmark experiment on the constructed classification dataset, ConvNeXt-Tiny achieves the best performance among the compared mainstream classifiers. These results demonstrate that AIS association can provide reliable category supervision for SAR ship classification, and the trained classifier can perform ship classification using SAR imagery alone. Full article

Background/Objectives: Ankle dorsiflexion plays a fundamental role in gait stability, impact absorption, and the stance-to-swing transition, and its impairment is a major limitation in transtibial prostheses. This study proposes and evaluates a lightweight two-stage pipeline for generating ankle-dorsiflexion references using a single shank-mounted inertial measurement unit (IMU). Methods: In the first stage, a deep neural network (DNN) estimates the shank pitch waveform from raw three-axis accelerations and angular velocities. In the second stage, the estimated shank pitch is transformed into an ankle-dorsiflexion waveform using a temporal mapping model. The approach was evaluated on a multisubject subset of the NONAN GaitPrint database comprising 35 healthy young adults, 598 walking trials, and approximately 122,468 gait cycles, using a strict subject-held-out protocol. Results: A feature-based Random Forest baseline showed limited performance, whereas the waveform-based DNN achieved high accuracy for shank pitch estimation, with test R² values up to 0.97. A conventional polynomial mapping between shank pitch and dorsiflexion yielded weak performance, whereas a temporal mapping model substantially improved the estimation of ankle dorsiflexion, with test R² values up to 0.85. The resulting ankle reference was integrated into a Gazebo/Robot Operating System 2 (ROS 2) simulation of a transtibial prosthesis, where the generated trajectories were executed in a software integration test under open-loop position control, confirming stable and consistent trajectory execution. Conclusions: These results indicate that combining accurate shank pitch estimation with temporal mapping enables feasible ankle-dorsiflexion reference generation from a single sensor in able-bodied gait, offering a preliminary, simulation-based pathway for single-sensor artificial intelligence (AI) pipelines in prosthetic development. The framework supports waveform-level feasibility, not clinical readiness or functional prosthetic control. Full article

In increasingly complex navigation environments, maritime traffic supervision needs to look beyond the instantaneous collision risk of individual-ship pairs. A multi-ship scene may become difficult to monitor because of vessel aggregation, spatial compression, encounter urgency, and inconsistent motion states. To support proactive Vessel Traffic Services (VTS), this study proposes a prediction-driven framework for assessing multi-ship traffic pressure by combining AIS-based short-term motion prediction with a Spatio-Temporal Encounter Traffic Pressure Index (ST-TPI). In the proposed framework, cleaned and resampled AIS trajectories are used to train an LSTM model for short-term vessel motion prediction. The predicted vessel states are then synchronized into future multi-ship traffic snapshots over a 30 min horizon, and ST-TPI is used to evaluate traffic pressure at the ship-pair, individual-ship, regional, and scene levels. Different from conventional collision-risk or traffic-complexity methods, the proposed framework focuses on how future traffic pressure forms, changes, and is transferred among vessels and vessel pairs. The method was tested using five typical multi-ship scenarios and a real-waterway case in the western precautionary area of the Laotieshan Channel. The prediction results showed stable short-term forecasting performance with low meter-level position errors under the observation-updated rolling evaluation, providing a basis for future multi-ship snapshot generation. The typical scenarios revealed different pressure-evolution patterns, including low-pressure persistence, temporary compression and release, delayed crossing pressure, complex interaction release, and High-level pressure formation. The real-waterway case further showed low and Low-medium pressure fluctuations, local pressure peaks, pressure release, and pressure-source transfer under practical AIS conditions. Prediction-error perturbation analysis indicated that the main high-pressure vessel pairs and pressure-level interpretations remained stable under tested position perturbations. Consistency analysis further showed that ST-TPI scene pressure was significantly correlated with conventional CRI-based encounter-risk indicators. These results indicate that the proposed framework can provide interpretable information on future pressure-evolution and dominant pressure sources, supporting proactive monitoring, early warning, and traffic organization in complex waterways, and contributing to a safer maritime traffic environment. Full article

Modeling a user’s evolving goals, values, and affect over time is central to perspective-aware AI, yet progress is bottlenecked by the lack of longitudinal data with ground-truth labels for the latent identity state. We introduce PAiNT (Perspective-Aware AI Identity and Narrative Toolkit), a generative framework that simulates long-horizon persona trajectories and emits corresponding multimodal artifacts with ontology-aligned labels of the latent identity state that produced them. PAiNT decouples identity dynamics from artifact generation via a typed Persona Matrix and Situation Graph, coordinated through a multi-agent loop with validation-gated transitions and bounded-window history conditioning. Across four personality archetypes, four backbone LLMs, and three architectural ablations, evaluated with a nine-metric suite calibrated on published longitudinal data, we find that (i) persona initialization produces a durable identity signal that persists above stochastic event noise; (ii) multi-agent orchestration and history conditioning govern distinct quality dimensions, with removal of either causing different failure modes; and (iii) a coherence frontier constrains the trade-off between temporal resolution and horizon, with substantial penalties at daily granularity. We release PAiNT and PAi-Bench, a human-validated benchmark of 1200 labeled multimodal artifacts. Full article

Under 10 min sparse Automatic Identification System (AIS) sampling, the reliability of point-wise motion statistics degrades substantially, and conventional classification methods rely on trajectory interpolation, which may introduce spurious motion patterns. This study proposes a feature-driven framework for fishing vessel type identification that eliminates the need for interpolation preprocessing. A 39-dimensional feature set is constructed using robust statistics, including the median and interquartile range, to characterize trajectory-level behavioral patterns. Adaptive speed interval thresholds are derived through a data-driven approach grounded in Bayesian decision boundaries, thereby removing the dependence on manually defined cut-off values. A backward ablation procedure guided by feature importance ranking identifies a lightweight 12-dimensional feature subset that retains 98.7% of the classification accuracy at a compression rate of 69%. Evaluated on 18,320 fishing vessel trajectories in the East China Sea, the full 39-dimensional feature set achieves a 5-fold cross-validation accuracy of 91.92% (Macro-F1 = 0.919, Kappa = 0.879), with inter-fold standard deviations ranging from 0.002 to 0.004. Comparative experiments demonstrate that three tree-based classifiers all exceed 90% accuracy on the same feature set, confirming that feature robustness, rather than model selection, constitutes the dominant performance factor. LightGBM achieves the optimal trade-off between accuracy and training efficiency, whereas the cross-validation standard deviation of LSTM is approximately 7.5 times greater, indicating that hand-crafted robust features provide superior stability under sparse sampling conditions. The proposed framework requires no fishery-specific prior knowledge and offers a transferable paradigm for sparse AIS trajectory analysis. Full article

With the rapid development of cross-border collaboration, intelligent port construction, and international logistics networks, large volumes of multisource heterogeneous data are continuously generated during cross-border circulation. To address the limitations of traditional financial review and compliance auditing methods in characterizing multisource signal coupling, as well as the tendency of conventional deep models to rely on spurious correlated features with insufficient interpretability, a multisource sensing signal fusion and causally explainable risk identification framework is proposed for cross-border trade anomaly detection. In this framework, electronic trade texts, structured financial declaration fields, GPS/AIS trajectories, port weighing records, RFID data, electronic seal status, X-ray inspection images, cold-chain temperature and humidity records, and vibration data are uniformly modeled as multisource sensing signals in cross-border trade and circulation processes. Subsequently, collaborative representation among textual semantics, attribute fields, logistics status, device records, and entity relationships is achieved through a cross-modal alignment mechanism. On this basis, an engineering-constraint-guided causal risk representation module is designed to reduce the interference of spurious correlated factors, such as regions, ports, transportation modes, and textual styles, in model decisions. Meanwhile, a counterfactual anomaly response module is introduced to analyze the influence of key variable changes on risk outputs, thereby enhancing the model’s ability to identify and explain true anomaly-driving factors. Experimental results show that the proposed method achieves the best overall performance in the cross-border trade anomaly detection task, with Accuracy, Precision, Recall, F1-score, AUC, and PR-AUC reaching $0.927$, $0.842$, $0.811$, $0.826$, $0.958$, and $0.817$, respectively, clearly outperforming baseline models including Logistic Regression, Random Forest, XGBoost, BERT, BERT+MLP, and Multimodal Transformer. In cross-time, cross-region, cross-port, and cross-entity testing scenarios, high F1-score and AUC values are still maintained. Under complex conditions such as text noise, missing modalities, logistics trajectory perturbations, and missing sensing records, only limited performance degradation is observed. Ablation experiments further verify the effective contributions of cross-modal attention, contrastive alignment, causal financial debiasing, counterfactual response, and engineering constraints to performance improvement. Full article