Search Results (515)

The increasing interest in the development and integration of navigation and positioning services across a wide range of receivers has exposed them to various security threats, including GNSS jamming and spoofing attacks. Early detection of jamming and spoofing interference is crucial to mitigating these threats and preventing service degradation. This research introduces an interference detection technique leveraging an AI algorithm applied to GNSS data utilizing various methods to enhance detection accuracy and efficiency. The objective was to use modern sensors and AI to develop an effective tool that detects, characterizes, and localizes interference, thereby reducing associated risks. These sensors and algorithms enable continuous GNSS interference monitoring and support real-time Decision-making. A server plays a crucial role in managing the entire system. Its primary function is to process data collected from various sensors referred to as nodes (e.g., static, rover, drone, and space) and from (public) GNSS networks as well as to perform localization using rotating-antenna nodes. Within the interference detection module, various methods were implemented at different points in the software receiver architecture. Each method’s certainty in identifying an interference source depends on its design and capabilities, with outcomes—whether positive or negative—being subject to potential accuracy or errors. To enhance the Decision-making process, an AI-based Decision-making block has been introduced to determine the presence of interference at a given epoch. The proposed interference monitoring methods were evaluated through experiments using GNSS signals under clean, jamming, and spoofing scenarios. The results demonstrate the techniques’ applicability across diverse scenarios, achieving high performance in interference detection, characterization, and localization. Full article

Urban green space (UGS) is a critical component of sustainable cities and a modifiable determinant of mental health (MH). This review synthesizes 93 empirical studies and 929 bibliometric records to map theoretical advances, methodological evolution, and governance implications in the UGS–MH field. We integrate the following six validated pathways into a unified socio-ecological framework: attention restoration, stress recovery, behavioral activation, physiological regulation, social cohesion, and environmental buffering. Methodological trends indicate a shift from static greenness proxies to street-view and multimodal exposure measures, and from cross-sectional correlations to models that address spatial heterogeneity, causal identification, and AI-enabled prediction. Bibliometric mapping reveals increasing interdisciplinarity, geographic diversification, and growing attention to dynamic exposure science. Persistent challenges include spatial and temporal misalignment between exposure and outcome measures, reliance on single-modality indicators, limited causal inference, and constrained cross-cultural generalizability. Building on these findings, we propose a governance-oriented framework to support sustainable and healthy cities through equitable green access, behavior-informed planning, nature-based interventions, and data-driven decision support. Overall, this review strengthens the bridge from evidence to action at the interface of urban sustainability and population mental health. Full article

The growing demand for high-fidelity, multi-parameter, distributed sensing in critical domains such as structural health monitoring, oil and gas exploration, and secure perimeter surveillance is pushing traditional optical fiber sensors (OFS) to their performance limits. Although conventional multiplexing techniques such as time-division and wavelength-division multiplexing (TDM, WDM) have been commercially successful, they are rapidly approaching fundamental bottlenecks in sensor density, spatial resolution, and data capacity. This review argues that the synergistic convergence of space-division multiplexing (SDM) and artificial intelligence (AI) represents a paradigm shift, enabling a new generation of intelligent, high-dimensional sensing networks. We comprehensively survey the state of the art in SDM-based OFS, detailing the operating principles and applications of multi-core fibers (MCFs) for ultra-dense sensor arrays and 3D shape sensing, as well as few-mode fibers (FMFs) for mode-division multiplexing and enhanced multi-parameter discrimination. However, the unprecedented spatial parallelism provided by SDM introduces significant challenges, including inter-channel crosstalk, complex signal demultiplexing, and massive data volumes. This paper systematically explores how AI, particularly machine learning (ML) and deep learning (DL), is being leveraged not merely as a tool but as an indispensable core technology to mitigate these impairments. We critically analyze AI’s role in digital crosstalk suppression, intelligent mode demultiplexing, signal denoising, and solving complex inverse problems for parameter estimation. Furthermore, we highlight how this AI–SDM synergy enables capabilities beyond the reach of either technology alone, such as super-resolution sensing and predictive analytics. The discussion is extended to include the critical supporting pillars of this ecosystem, such as advanced interrogation techniques and the associated data management challenges. Finally, we provide a forward-looking perspective on the trajectory of the field, outlining a path toward cognitive sensing networks that are self-calibrating, adaptive, and capable of autonomous decision-making. This review is intended to serve as a foundational reference for researchers and engineers at the intersection of photonics and intelligent systems, illuminating the pathway toward tomorrow’s intelligent sensing infrastructure. Full article

Global Digital Elevation Models (DEMs) exhibit systematic biases constrained by acquisition geometry and surface penetration. This study aims to evaluate whether the increasing complexity of geometric deep learning (e.g., Graph Neural Networks, GNNs) is justified by performance gains over established feature engineering paradigms (e.g., XGBoost) under the constraints of sparse altimetry supervision. We established a rigorous comparative framework across four mainstream products—ALOS World 3D, Copernicus DEM, SRTM GL1, and TanDEM-X—using Sichuan Province, China, as a representative natural laboratory. Our results reveal a fundamental scale mismatch (where the ~485 m average spacing of sampled altimetry footprints dwarfs the local terrain resolution): despite their topological complexity, Hybrid GNN models fail to establish a statistically significant accuracy advantage over the systematically optimized XGBoost baseline, demonstrating RMSE parity. Mechanistically, we uncover a critical divergence in decision logic: XGBoost relies on a stable “Physics Skeleton” consistently dominated by deterministic features (terrain aspect and vegetation density), whereas GNNs exhibit severe “Attribution Stochasticity” ($\rho \approx$ 0.63–0.77). The GNN component acts as a residual-dependent latent feature learner rather than discovering universal topological laws. We conclude that for geospatial regression tasks relying on sparse supervision, “Physics Trumps Geometry.” A “Feature-First” paradigm that prioritizes robust, domain-knowledge-based physical descriptors outweighs the indeterminate complexity of “Black Box” architectures. This study underscores the imperative of prioritizing explanatory stability over marginal accuracy gains to foster trusted Geo-AI. Full article

Endometrial cancer (EC) is the most common gynaecologic malignancy in developed countries, and its diagnostic and prognostic framework has evolved substantially following the introduction of the 2023 FIGO staging system, which integrates molecular classification with clinicopathologic features. Both histopathologic features, such as lymphovascular space invasion (LVSI) and molecular subtype, including POLE mutation status, mismatch-repair deficiency, and p53-abnormal phenotype, are incorporated into the updated staging system, highlighting the importance of tumour biology in risk stratification. Accordingly, the value and contribution of MRI to patient management must extend beyond macroscopic assessment to support a more biologically driven approach. This narrative review synthesizes recent advances in MRI for EC, highlighting developments that improve diagnostic accuracy and align imaging with the molecular paradigm. Multiparametric MRI remains the reference standard for local staging, while emerging quantitative diffusion techniques provide microstructural biomarkers associated with tumor aggressiveness and prognostic features. The consistency of nodal staging has been enhanced by Node-RADS, a structured reporting system that integrates nodal morphology and configuration, with the goal of improving reproducibility and diagnostic performance over size-based assessment alone. Radiomics and artificial intelligence (AI) represent the most transformative frontier, enabling MRI to infer biological behaviours previously accessible only via histopathologic assessment. Radiomics and deep-learning models have demonstrated high accuracy in predicting LVSI, DMI, nodal metastasis, and molecular subtypes, offering non-invasive biomarkers aligned with FIGO 2023 prognostic categories. Together, these advances position MRI as a quantitatively enriched, biologically relevant tool that supports precision oncology in endometrial cancer. Full article

Chinese font generation is important for digital typography, cultural preservation, and personalized user interfaces. However, existing methods often face challenges in maintaining structural consistency, supporting diverse stylistic variations, and achieving computational efficiency simultaneously, especially in cloud-based environments. A key application is bandwidth-efficient font delivery, where compact structural templates replace large font files for on-demand style customization. To address these issues, this paper proposes Stroke2Font—a hierarchical vector model with AI-driven optimization for dynamic Chinese font generation. The core model decouples structural representation from style rendering through stroke element decomposition and Bézier curve parameterization. To further balance structural fidelity, style diversity, and real-time performance, we introduce a three-module optimization framework: (1) a reinforcement learning policy for dynamic selection of Bézier control parameters to minimize rendering latency; (2) a genetic algorithm for exploring style vector spaces and generating novel font variants; and (3) an adaptive complexity-aware optimization strategy that dynamically configures parameters based on character structural complexity. Experimental results on a dataset of 150 Chinese characters with 1123 stroke trajectories and 5287 feature points demonstrate that the adaptive complexity-aware optimization achieves the highest trajectory similarity of 65.2%, representing a 6.4% relative improvement over baseline (61.3%). The evaluation covers characters ranging from 1 to 18 strokes across 6 stroke types, with standard deviation reduced to ±5.7% (compared to ±6.5% baseline), indicating more consistent performance. Quantitative analysis confirms that the method generalizes effectively across varying character complexity, with the optimization showing stable improvement regardless of stroke count distribution. These results validate that Stroke2Font provides an effective solution for high-quality, efficient, and scalable Chinese font generation in cloud-based applications. Full article

Segmented telescopes face significant challenges in achieving high segment positioning accuracy under complex disturbances, which directly impact observational sensitivity and resolution. Conventional rigid actuators with limited bandwidth (e.g., Keck ~20 Hz) struggle to maintain control stability. Novel dual-stage actuators combining coarse and fine adjustment (e.g., voice coil motors) now achieve <8 nm precision over millimeter-level strokes. Moreover, their higher closed-loop bandwidth (e.g., TMT ~60 Hz) can ensure rapid settling without overshoot and robust suppression of high-frequency disturbances (e.g., pulsating wind and mechanical vibration). In parallel, system-level control strategies have been updated accordingly. Ground-based systems focus on real-time multimodal decoupling, while space-based systems emphasize non-contact vibration isolation and nested multi-loop control to achieve sub-arcsecond pointing stability. This review surveys the design and control strategies of damping–positioning mechanisms for segmented telescopes and discusses the key trade-offs among critical performance metrics, including resolution, stroke, and load capacity. Particular attention is given to the disturbance-sensitivity analysis and active damping techniques (up to ~50% vibration reduction) implemented in the ELT “hard” actuator approach. Future directions include cross-scale collaborative control, smart material applications, and AI-based adaptive parameter optimization, which together provide a technical pathway toward high-precision imaging in next-generation highly segmented telescopes. Full article

Ensuring space environment security through the detection of space debris and non-cooperative threat objects has become a critical mission for next-generation spaceborne surveillance systems. Frequency diversity array (FDA) radar, with its unique range angle-dependent beampattern, offers a transformative capability to distinguish closely-spaced space threats from intense background clutter. However, the operational deployment of spaceborne FDA is inherently hindered by stringent platform resource constraints, including limited power supply, high hardware complexity, and restricted data transmission bandwidth. These physical limitations inevitably lead to incomplete signal observations, resulting in elevated sidelobes that can obscure small, high-speed space debris. To bridge the gap between hardware constraints and high-fidelity surveillance, this paper proposes an AI-enabled data recovery framework based on deep matrix factorization. Specifically designed to process the complex-valued nature of radar echoes, the proposed framework introduces two specialized architectures: a real-valued representation-based method (DMF-Rr) and a native complex-valued deep matrix factorization (CDMF) network that preserves vital phase coherence. By leveraging deep learning to “enable” sparse-sampled systems, the proposed method effectively reconstructs missing observations without requiring prior knowledge of the signal rank. Numerical results demonstrate that the AI-powered CDMF significantly suppresses the high sidelobes induced by resource-limited sampling, enabling the reliable identification and localization of weak threat objects. This study demonstrates the power of AI in overcoming the physical bottlenecks of spaceborne hardware, providing a robust solution for enhancing space situational awareness in an increasingly crowded orbital environment. Full article

Idiopathic normal pressure hydrocephalus (iNPH) is a potentially reversible cause of gait disturbance and cognitive impairment in older adults, yet its diagnosis remains challenging and controversial. The core difficulty lies in distinguishing true hydrocephalus from ventricular enlargement secondary to cerebral atrophy or neurodegenerative disease, a distinction now recognized as non-binary. In many patients, ventricular enlargement reflects a continuum ranging from predominantly hydrocephalic iNPH to mixed pathological states combining impaired cerebrospinal fluid (CSF) dynamics and neurodegeneration. Conventional neuroradiological markers, including the Evans Index, the callosal angle, and the disproportionately enlarged subarachnoid-space hydrocephalus (DESH) pattern, provide useful qualitative guidance but are limited by their two-dimensional nature, interobserver variability, and poor sensitivity for differential diagnosis and outcome prediction. Over the past decade, advances in artificial intelligence-based brain volumetry (AI-BrV) have introduced a new paradigm for quantitative structural assessment. By enabling automated, anatomically precise, and reproducible three-dimensional quantification of ventricular and extraventricular CSF, cortical and subcortical gray matter, deep gray matter nuclei, and periventricular white matter, AI-BrV addresses many limitations of traditional imaging approaches. Beyond absolute volume measurements, AI-BrV enables the derivation of composite indices and ratios that may capture disease-specific structural phenotypes and better reflect the underlying pathophysiology of ventricular enlargement. Importantly, AI-BrV pipelines can be applied retrospectively to large legacy neuroimaging datasets and compared with extensive publicly available repositories, facilitating normative modeling, cross-disease analyses, and external validation of volumetric biomarkers. When integrated with clinical data and multivariable statistical or machine-learning frameworks, these approaches hold promise for improving patient selection, refining disease categorization, and supporting more rational decision-making regarding CSF diversion. In this context, AI-BrV offers a unifying framework for reconciling divergent clinical perspectives and advancing iNPH toward a more precise, reproducible, and evidence-based diagnostic and therapeutic paradigm. Full article

Multimodal human–AI systems generally consider facial expressions and body motions as separate input streams, leading to disjointed interpretations and diminished emotional coherence. To overcome this issue, we offer the Engagement-Safe Expressive Alignment (ESEA) paradigm and the Unified Visual Synchrony (UVS) framework as its computational implementation. UVS models the coherence between facial expressions and gestures, offering an interpretable visual synchrony signal that can function as adaptive feedback in human–AI interactions. The framework’s key component is the Consistency Index for Affective Synchrony ($CIAS$), which correlates brief visual segments with scalar synchrony scores through a common latent representation. Facial and gestural signals are processed by modality-specific projection networks into a unified latent space, and $CIAS$ is derived from the similarity and short-term temporal consistency of these latent trajectories. The synchrony index is regarded as an estimation of affective visual coherence within the ESEA paradigm. We formalize the UVS/$CIAS$ framework and conduct a comparative experimental evaluation utilizing matched and mismatched face–gesture segments derived from rendered dialog footage. Utilizing ROC analysis, score distribution comparisons, temporal visualizations, and negative control tests, we illustrate that $CIAS$ effectively captures structured face–gesture alignment that surpasses similarity-based baselines, while also delivering a persistent, time-resolved synchronization signal. These findings establish $CIAS$ as a principled and interpretable feedback signal for future affect-aware, engagement-focused multimodal agents. Full article

This paper explores how semantic-space reasoning, traditionally used in computational linguistics, can be extended to tactical decision-making in team sports. Building on the analogy between texts and teams—where players act as words and collective play conveys meaning—the proposed methodology models tactical configurations as compositional semantic structures. Each player is represented as a multidimensional vector integrating technical, physical, and psychological attributes; team profiles are aggregated through contextual weighting into a higher-level semantic representation. Within this shared vector space, tactical templates such as high press, counterattack, or possession build-up are encoded analogously to linguistic concepts. Their alignment with team profiles is evaluated using vector-distance metrics, enabling the computation of tactical “fit” and opponent-exploitation potential. A Python-based prototype demonstrates how these methods can generate interpretable, dynamically adaptive strategy recommendations, accompanied by fine-grained diagnostic insights at the attribute level. Evaluation through synthetic scenarios and a pilot study with real match data establishes internal consistency and feasibility of the approach; operational validity in live coaching contexts remains an open question for future prospective validation. Beyond football, the framework offers a potentially generalizable approach for collective decision-making in team-based domains—ranging from basketball and hockey to cooperative robotics and human–AI coordination systems. The paper concludes by outlining future directions toward real-world data integration, predictive simulation, and the validation work required before operational deployment. Full article

This study conducts a bibliometric and content analysis of ‘artificial intelligence-enabled auditing’ over three decades. The use of artificial intelligence (AI) tools in auditing has evolved and is now an imperative practice in the auditing space. Using bibliometric methods via Bibliometrix R-package (Biblioshiny) and VOSviewer, this research mainly examines the scholarly discussion on AI-enabled auditing, using the Scopus database. The main themes identified are: Theme 1: AI in auditing: readiness, representation, and implementation; Theme 2: data-driven audit ecosystems and digital technologies; and Theme 3: audit quality, professional skepticism, and ethical governance. On the descriptive end, publication trends, prominent authors, articles, and sources are identified. The findings highlight a significant increase in AI-enabled auditing studies since 2018, coinciding with growing global awareness on the importance of AI across all spheres of business. The outcome of this research contributes to a wide array of stakeholders, including businesses, audit firms, shareholders, and policymakers; it should give insights to business organizations on the capabilities of AI-assisted auditing, while policymakers should have access to verifiable, auditable and regulatory-compliant systems for the implementation of their regulations. Investors may further enhance their knowledge in terms of how AI-assisted auditing increases the quality of their investment decisions and, at the same time, the risks involved. Finally, auditing firms should further invest in improving the application of technology in the auditing environment and ensure quality, evidence-based audit outcomes, and reporting. Full article

As virtual reality technologies evolve toward widespread adoption in education, industry, and social communication, their increasing complexity exposes new and often overlooked security challenges. Immersive environments collect continuous multimodal data, including motion tracking, gaze, voice, and biometric indicators that extend far beyond traditional computing attack surfaces. This paper synthesizes recent research (2023–2025) on cybersecurity, privacy, and behavioral safety in virtual reality (VR) systems, identifies the main vulnerabilities, and proposes a unified defense architecture: the three-layer VR Security Framework (TVR-Sec). Through comparative review and conceptual integration of 31 peer-reviewed studies, three interdependent protection domains emerged: (1) System Integrity, securing hardware, firmware, and network communications against spoofing and malware; (2) User Privacy, ensuring the ethical management of biometric and behavioral data through federated learning and consent-based control; and (3) Socio-Behavioral Safety, addressing harassment, manipulation, and psychological exploitation in shared virtual spaces. The framework situates VR security as a multidimensional adaptive process that combines technical hardening with human-centered defense and ethical design. By aligning cyber–human protections through an AI-driven monitoring and policy engine, TVR-Sec advances a holistic paradigm for securing future immersive ecosystems. Full article

Information and communication technologies have evolved exponentially in recent years, significantly expanding their diversification and applicability. Extended reality (XR) technologies—including virtual, augmented, and mixed reality—have solidified the conceptualization of space and function. XR represents the definitive medium due to its close analogy with physical reality, enabling an unprecedented degree of interaction compared to previous media. By leveraging spatial and temporal factors, XR allows for the emergence of suprainteractions—interactions that do not occur naturally in physical environments. The integration of AI into these workflows heralds a new era, reevaluating technological utility as the current landscape poses challenges for identifying use cases and dead zones within the XR field. This article proposes a model, derived from a narrative literature review, that identifies key features in technological applications and the evolution of XR. Based on concepts such as representativeness, realism, system performance, and spatial narrative, the model designs a framework for the development of diverse functions within the XR domain. Full article

The pervasive adoption of AI and AIoT applications at the network edge presents both opportunities and challenges for smart cities. With a focus on the energy efficiency of AI in urban environments, this paper provides a systematic comparative analysis of representative edge hardware platforms, i.e., embedded GPUs, FPGAs, and ultra-low-power microcontroller-/sensor-class devices, assessing their suitability for AI workloads in IoT-driven smart city infrastructures. The evaluation, based on direct characterization of diverse neural networks and relevant datasets, quantifies computational performance and energy behavior through inference latency, throughput, and energy/per inference measurements. Across the evaluated network–board pairs, the measured inference power spans several orders of magnitude, ranging from 0.1–10 mW for ultra-low-power Intelligent Sensor Processing Units (ISPUs) up to 1–10 W for embedded GPUs, highlighting the wide design space between the least and most power-demanding configurations. Results indicate that embedded GPUs provide a favorable performance-to-power ratio for computationally intensive workloads, while MCU/ISPU-class solutions, despite throughput limitations, offer compelling advantages in ultra-low-power scenarios when combined with quantization and pruning, making them well-suited for distributed sensing and actuation typical of smart city deployments. Overall, this comparative analysis guides hardware selection for heterogeneous, sustainable AI-enabled urban services. Full article