Search Results (2,612)

Digital images are easily transmitted in Internet, but there is also a great risk of information leakage. To meet the requirements of secure image transmission and real-time communication, an image encryption algorithm based on a novel chaotic map and a three-dimensional histogram is proposed. Firstly, a novel two-dimensional chaotic map is designed. Compared with traditional chaotic systems, it exhibits superior chaotic performance and a wider parameter range; secondly, the proposed algorithm is designed to extend the original image to three dimensions, followed by 3D simultaneous scrambling–diffusion; thirdly, the 2D exclusive OR (XOR) operation is performed for further diffusion; finally, the 3D matrix is merged to obtain the encrypted image. The encrypted images have uniform histograms and pass the Chi-square test. Information entropy is greater than 7.9992, and the average values of Number of Pixels Change Rate (NPCR) and Uniform Average Change Intensity (UACI), being 99.6137 and 33.4783, respectively, show that this algorithm can effectively resist differential attacks. On average, a 512 × 512 image can be encrypted in 0.7 s using the proposed algorithm. Thus, the proposed algorithm is applicable to image transmission over network platforms due to its high security, excellent encryption performance, and high efficiency. Full article

The sixth generation of mobile networks (6G) represents a paradigmatic shift in the conception of wireless communication systems, where Artificial Intelligence (AI) is not integrated as an additional feature but is conceived as a native and fundamental component of the physical layer (PHY). This paper presents a comprehensive survey of the state of the art in AI-native physical layer for 6G, synthesizing approximately 100 references from the period 1948–2025. The survey systematically covers 5 main PHY components (channel coding, channel estimation, signal detection, beamforming, and semantic communications) and analyzes 8 AI architectural families (autoencoders, CNN, RNN/LSTM, Transformers, GNN, GAN, Diffusion Models, and Foundation Models), addressing theoretical foundations, proposed architectures, learning algorithms, implementation challenges, and future research directions. A rigorous mathematical framework underpinning these developments is presented, including optimization formulations, convergence analysis, and theoretical performance characterization. Published results from the literature demonstrate that AI-native physical layer can improve conventional performance metrics and enable emerging capabilities essential to 6G, such as semantic communications, predictive environmental adaptation, and operation in previously inaccessible computational complexity regimes. However, such gains are conditional on adequate training resources, robust channel-matched data, and careful consideration of known limitations including generalization across channel distributions, sample inefficiency, model interpretability, and hardware implementation constraints—all of which are critically analyzed in this survey. A reproducible proof-of-concept benchmark further confirms that, under severe resource constraints, autoencoder-based codes currently underperform conventional schemes, highlighting the gap between theoretical potential and practical deployment readiness. Full article

The Industrial Internet of Things (IIoT) is central to modern industrial automation, yet its growing connectivity exposes critical systems to evolving cyber threats. Traditional intrusion detection methods struggle in IIoT environments due to class imbalance and limited adaptability to zero-day attacks. This systematic review evaluates generative AI techniques for IIoT intrusion detection and identifies deployment requirements for industrial environments. We searched five databases (IEEE Xplore, ACM Digital Library, Springer, ScienceDirect, and arXiv) for studies published between January 2019 and December 2025, applying predefined inclusion criteria. Following a systematic selection process (identification plus three progressive screening stages) across 342 records, 42 primary studies were included for systematic synthesis. We examined four GenAI paradigms—Generative Adversarial Networks, Transformers, Diffusion Models, and Variational Autoencoders—analyzing nine state-of-the-art frameworks through comparative performance analysis. Hybrid Transformer architectures (e.g., Transformer-GAN-AE) achieve the most consistent detection performance, while diffusion-based models (e.g., Diff-IDS) provide computational advantages for edge deployments. However, substantial variability in evaluation methodologies and limited reporting of statistical rigor indicate important gaps in current research practices. These findings inform the development of GenAI-driven strategies tailored to industrial infrastructure constraints and highlight key directions for advancing IIoT cybersecurity. Full article

Optical fiber sensors deployed in harsh industrial fields, e.g., high-temperature wet-oxygen, face severe challenges in signal attenuation and mechanical degradation. While amorphous silicon oxycarbide (a-SiOC) coatings offer a promising solution due to their adjustable thermo-mechanical properties, balancing their structural density with environmental stability remains a critical technical bottleneck. In this study, a-SiOC coatings were deposited on optical fibers using hexamethyldisilane (HMDS) and trace oxygen via radio-frequency capacitively coupled plasma-enhanced chemical vapor deposition (PECVD). A systematic investigation was conducted to determine the impact of deposition temperature (70–420 °C) on the precursor dissociation kinetics, microstructural evolution, and corrosion resistance of the coatings. An elevation in temperature promotes the elimination of organic terminal groups (–CH₃, –H) and enhances surface diffusion, driving the coating from a loose, carbon-rich “polymer-like” structure (dominated by Si–C bonds) to a dense, inorganic “silica-like” skeleton (dominated by Si–O–Si bonds). High-temperature corrosion tests in a wet-oxygen environment (500–900 °C) demonstrate that the failure mechanism is highly dependent on deposition temperature. Coatings deposited at low temperatures suffer catastrophic cracking due to pronounced oxidative shrinkage and the release of volatile species, whereas coatings deposited at 420 °C exhibit microcracking caused by severe carbon phase separation and stress concentration within the rigid inorganic network. In the present system, 350 °C is identified as the optimal deposition temperature, as it achieves the best balance of network densification and structural flexibility, while exhibiting the best mechanical performance. Full article

Alginate hydrogels offer mild ionic gelation and tunable porosity for drug delivery, yet their hydrophilic, macroporous networks suffer from rapid initial burst release of water-soluble payloads. Here we introduce a hierarchical barrier-engineering strategy in which poly(D,L-lactide-co-glycolide)/poly(ethylene glycol) (PLGA/PEG) blend coatings are applied via dip-coating to Ca²⁺-cross-linked alginate beads (~1 mm) and microgels (~100 µm). For beads, three-cycle PLGA/PEG multilayer coating suppressed the initial swelling rate (dQ/dt) by ~50% and reduced 1 h burst release from >85% to ~60%, functioning as an “early-burst buffer” rather than a long-term depot. For microgels, a single PLGA/PEG layer partially attenuated burst release; however, an additional PLGA outer shell (double-barrier architecture) shifted the release-governing mechanism from swelling-dominated to diffusion-barrier-dominated control, limiting 10 min release to <10%. Core–shell formation was verified by confocal laser scanning microscopy (CLSM), scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM/EDS), Fourier-transform infrared spectroscopy (FT-IR), and X-ray photoelectron spectroscopy (XPS); thermogravimetric analysis (TGA) showed ~73–79% coating retention after 9 days in phosphate-buffered saline (PBS, 37 °C). A vacuum re-loading process further improved encapsulation efficiency (>50% for beads, >20% for microgels) without compromising gel integrity. In beads, burst control was governed by swelling suppression; in microgels, the additional PLGA shell shifted control to diffusion-barrier-dominated release, demonstrating that barrier architecture must be adapted to particle scale. Full article

Digital payment and settlement markets operate as interconnected financial systems shaped by institutional, technological, and capability-based elements. This study examines how digital transformation and digital financial inclusion interact within this system to influence Sustainable Digital Payment and Settlement Market Performance (SDPSMP), with DeFi adoption capability acting as a structural translation mechanism and AI and big data analytics functioning as adaptive enablers. Integrating the Resource-Based View and Diffusion of Innovation, the study explains why technology diffusion does not consistently produce stable market-level outcomes. Cross-sectional data were collected from 422 professionals in Saudi financial institutions engaged in payment, settlement, and FinTech functions. A dual-stage SEM–ANN approach was employed, using PLS-SEM to test direct, mediating, and moderating effects and Artificial Neural Networks (ANN) to capture nonlinear predictive patterns. Results show that digital transformation and digital financial inclusion enhance DeFi adoption capability and directly improve SDPSMP. DeFi capability partially mediates both relationships. Analytics capability strengthens the effects of inclusion and DeFi capability on system performance but does not moderate the transformation–performance link. ANN findings identify analytics capability and financial inclusion as dominant predictors. The study advances understanding of digital payment markets as complex adaptive systems and provides evidence on how coordinated capability development supports long-term resilience and structural stability. Full article

Background: Reaction time (RT) is a fundamental measure of information processing speed in cognitive neuroscience and is influenced by both structural and functional brain properties. While prior studies have independently linked white matter microstructure and EEG alpha oscillations to cognitive performance, their joint contribution to distinct aspects of RT remains unclear. This study aims to investigate whether multimodal data can dissociate neural systems underlying cognitive and motor components of processing speed. Methods: We analyzed diffusion tensor imaging, resting-state individual EEG alpha peak frequency (IAF), demographic variables, and behavioral RT measures from a GO/NO-GO paradigm in 24 healthy adults from the Cuban Human Brain Mapping Project. Behavioral metrics included the mean, standard deviation and skewness of reaction times for simple and complex tasks. Sparse multiple canonical correlation analysis was applied to identify multivariate associations across modalities. Results: Two significant latent dimensions were identified. The first dimension linked bilateral fronto-temporal association tracts (SLF, IFOF, UNC) with complex RT performance, reflecting higher-order cognitive processing. The second dimension associated motor and interhemispheric tracts (CGC, CST, ILF, forceps major and minor) with intra-individual asymmetric variability (skewness) across tasks, indicating a motor-execution consistency system. IAF did not significantly contribute to either dimension. Sex showed strong associations with both components. Conclusions: Distinct white matter networks were associated with separable cognitive and motor aspects of processing speed, while resting-state alpha frequency did not show stable contributions with behavioral variability in this sample. IAF showed minimal contribution within the identified sparse multivariate dimensions. These findings highlight the importance of multimodal and multivariate approaches for understanding and potentially disentangling complex brain–behavior relationships. Full article

Hydrogen embrittlement is a critical degradation mechanism in microalloyed and pipeline steels used in hydrogen-economy infrastructure. We present a physics-informed neural network (PINN) framework that embeds Fick’s second law and the Arrhenius temperature dependence directly into the loss function, trained on 22 temperature-dependent data points spanning pure α-Fe and API X65 pipeline steels (modern and vintage microstructures). The PINN recovered the pure-iron activation energy (4.2 kJ mol⁻¹ vs. literature 4.15 kJ mol⁻¹, R² = 1.00) and yielded Arrhenius activation energies of 28.5 and 45.2 kJ mol⁻¹ for modern and vintage X65, respectively, indicating substantially stronger trapping in older microstructures. McNabb–Foster analysis of ten ternary Fe–Me–C,N alloys revealed flat-trap binding enthalpies of 19 ± 2 kJ mol⁻¹ and deep-trap free energies of 57 ± 2 kJ mol⁻¹, with effective diffusivities spanning three orders of magnitude governed primarily by flat-trap density. The framework provides a computationally efficient and physically consistent tool for hydrogen transport prediction, with a clear roadmap for multi-feature extension incorporating compositional and microstructural descriptors. Full article

Accurate Remaining Useful Life (RUL) prediction is pivotal for the intelligent operation and maintenance of high-precision equipment. However, existing deep learning-based prognostic methods predominantly focus on point estimations and often overlook the non-Markovian characteristics and stochastic uncertainties inherent in complex mechanical degradation. To bridge this gap, this study proposes a novel uncertainty-aware hybrid prognostic framework by synergizing TCN–Transformer architectures with fractional Brownian motion (FBM). Specifically, a TCN–Transformer hybrid network is developed to adaptively learn a multi-scale drift function, effectively capturing both localized causal features and global long-range temporal dependencies. Concurrently, the FBM component is employed to model the diffusion process, explicitly accounting for the long-range dependence and inherent stochasticity of degradation. By leveraging the first hitting time (FHT) principle, an approximate analytical expression for the RUL probability density function (PDF) is derived based on an established approximation treatment for FBM-driven degradation processes, enabling robust uncertainty quantification. Experimental results on both the XJTU-SY bearing dataset and the servo tool holder power head system dataset demonstrate that the proposed method achieves superior predictive accuracy and reliable uncertainty quantification, thereby providing effective support for condition-based maintenance and intelligent decision-making. Full article

Background: Brain tumors represent a major cause of neurological morbidity and mortality, often requiring radiotherapy as a central component of treatment. While advances in radiation techniques have improved tumor control, increasing attention has been directed toward radiation-induced effects on healthy brain tissue, particularly regarding neuronal function and cognitive outcomes. Objective: This review aims to provide a structured synthesis of current evidence on radiation-induced neuronal dysfunction, integrating dose–volume parameters, neuroimaging biomarkers, and clinical neurological manifestations. Methods: A structured literature review was conducted using electronic databases including PubMed, Scopus, and Web of Science. Relevant studies evaluating dose–volume effects, neuroimaging findings, and clinical outcomes following cranial radiotherapy were included. Results: Dose–volume histogram (DVH) parameters, including mean brain dose and intermediate-dose volumes (V10–V30), as well as hippocampal dose, were identified as key factors associated with cognitive decline and neuronal dysfunction. Conventional MRI detects structural changes such as white matter injury and radionecrosis, while advanced techniques including diffusion tensor imaging (DTI) and functional MRI (fMRI) reveal microstructural damage and network disruption. These imaging findings correlate with a spectrum of clinical manifestations ranging from subtle cognitive impairment to significant neurological deficits. Conclusions: Radiation-induced neuronal dysfunction represents a complex and multifactorial process that extends beyond localized tissue injury. Integrating dose–volume considerations with advanced imaging biomarkers may improve risk stratification and support the development of neuroprotective strategies in patients undergoing cranial radiotherapy. Full article

Glioblastoma multiforme (GBM) remains the most aggressive and therapeutically intractable primary brain tumor, with many patients experiencing rapid relapse despite maximal surgical resection followed by standard chemoradiation. This persistent failure reflects the convergence of profound tumor-intrinsic genetic heterogeneity and a highly dynamic, spatially structured, and immunosuppressive tumor microenvironment (TME). Together, these forces create strong selective pressures that fuel tumor evolution, intratumoral diversity, phenotype plasticity, diffuse invasion, and robust resistance to therapy. The TME of GBM is orchestrated through a complex interplay between diverse cellular constituents, including tumor-associated macrophages, reactive astrocytes, endothelial cells, pericytes, and GBM stem cells, and non-cellular components such as extracellular matrix remodeling, hypoxia, metabolic and nutrient gradients, and spatially patterned cytokine and chemokine signaling networks. Additionally, heterogeneity in blood–brain barrier (BBB) and blood–tumor barrier (BTB) complicates drug delivery and immune surveillance, reinforcing therapeutic resistance and regional tumor adaptation. Conventional two-dimensional cell cultures and animal models fail to sufficiently capture these multiscale, patient-specific interactions, limiting their translational predictive power. In this narrative review, we synthesize recent advances in GBM organoid technologies as physiologically relevant, three-dimensional platforms that more faithfully recapitulate TME for driving tumor evolution and treatment resistance. We compare complementary organoid strategies, including patient-derived GBM organoids that preserve native cytoarchitecture, cerebral organoid co-culture systems that reconstruct tumor–brain interactions, and advanced platforms incorporating immune and vascular features such as air–liquid interface cultures, microglia-enriched systems, and BBB/BTB-integrated models. Finally, we highlight emerging innovations such as spatial transcriptomics, organoid-on-a-chip systems, live imaging coupled with lineage tracing, genome engineering, and artificial intelligence integration that collectively position GBM organoids at the forefront of precision neuro-oncology, reproducing TME, enabling dynamic mapping of tumor evolution, and accelerating patient-specific therapeutic discovery. Full article

Background: Diffuse large B-cell lymphoma (DLBCL) is one of the most frequent lymphomas. To date, it is not possible to identify which DLBCL patients will have an aggressive clinical evolution only by using hematoxylin and eosin (H&E) histological images. Methods: This study predicted the prognosis of DLBCL using H&E images, computer vision and deep learning. The series included 114 DLBCL cases, split into 2 prognostic groups according to overall survival, and 44 cases of reactive lymphoid tissue. Results: The curve fitting and slope analysis showed a point of inflection at 2 years (24 months), which differentiated patients with aggressive clinical evolution (“Dead < 2 years”, b1 = −0.024) from the rest with moderate clinical evolution (“Others”, b1 = −0.003). Twenty different convolutional neural networks (CNNs) were used, and explainable artificial intelligence (XAI) was also applied. The final model based on DarkNet-19 predicted prognosis groups with high performance (test set accuracy = 96.3%). The other performance parameters were precision (94.5%), recall (95.0%), false positive rate (3.1%), specificity (96.9%), and F1 score (94.7%). XAI, including grad-CAM, occlusion sensitivity, and image-LIME, confirmed that the CNN focused on the correct areas. Hybrid partitioning to prevent information leakage with patient-based analysis, image classification between DLBCL and 44 cases of reactive lymphoid tissue, and hyperparameter tuning were also successfully performed. Correlation with the clinicopathological characteristics found that the Dead < 2 years group was correlated with stage III–IV, International Prognostic Index (IPI) High + High/intermediate, progressive disease, non-GCB cell-of-origin, CD10−, BCL2+, and Epstein–Barr virus (EBER)+. Analysis of the microenvironment, immune checkpoint, cell cycle, and germinal center markers showed that Dead < 2 years had higher IL10, PD-L1, and CD163 levels and lower E2F1 protein expression. No differences were found for Ki67, CSF1R, CASP8, TNFAIP8, LMO2, MYC, MDM2, CDK6, and TP53 markers at a quantitative level. Conclusions: The DLBCL overall survival can be predicted using H&E histological images and deep learning using the 2-year (24 months) point (similar to POD24). This trained CNN can be used as a pretrained model for transfer learning in the future. Full article

Supercritical carbon dioxide (SC-CO₂) fracturing has been recognized as an effective technology for developing unconventional oil and gas resources. The extent to which natural fractures can be activated is a critical factor controlling overall reservoir stimulation. A thorough understanding of the activation and propagation mechanisms of natural fractures during SC-CO₂ fracturing is therefore essential for elucidating fracture network evolution and optimizing stimulation strategies. In this work, a multiphysics-coupled numerical model for intersecting fracture propagation was developed using the phase-field method, incorporating formation pressure evolution and variations in CO₂ properties (density and viscosity). Based on this model, the influences of fracture approach angle, horizontal stress difference, injection temperature, and injection rate on fracture propagation patterns and pressure diffusion were systematically investigated. To quantitatively describe the stimulated reservoir volume, a “diffuse interface” was defined to represent the region affected by SC-CO₂ injection. The simulation results demonstrate that larger approach angles enhance the activation of natural fractures, with a 60° angle producing the maximum diffuse interface ratio of 72.5%. Although higher horizontal stress differences tend to suppress fracture activation, they promote plastic deformation at fracture tips, enlarging the diffuse interface to 86.72% at 15 MPa. Elevated injection temperatures further facilitate fracture propagation; as the temperature rises from 313.15 K to 403.15 K, the lateral fracture length increases from 2.8 cm to 3.7 cm, accompanied by continuous expansion of the diffuse interface. Under constant injection rate, a greater injection volume also enhances natural fracture activation and drives fractures to extend farther. These results provide theoretical insights for the design and optimization of SC-CO₂ fracturing in naturally fractured reservoirs. Full article

Fusion-based hyperspectral image super-resolution (HSI-SR) on diffusion models exhibits promising performance in generating high-quality, realistic features. However, existing methods are confronted with two limitations: (1) static conditional guidance is discordant with the dynamic denoising process, and (2) modality conflicts are inadequately addressed by concatenation. To address these challenges, we propose a novel Modulated Diffusion Framework with Spatial–Spectral Disentangled Guidance (SSDG). Specifically, it introduces a Dynamic Modulated Residual Network (DMRN), which leverages a time-aware mechanism to dynamically adjust conditional feature injection, ensuring adaptive guidance throughout all denoising stages. Furthermore, we design a training-free SSDG strategy to explicitly decouple spatial and spectral guidance during sampling, allowing for flexible control over the fusion process to mitigate modality conflicts. Extensive experiments on three public datasets demonstrate that the proposed method achieves state-of-the-art performance, exhibiting superior robustness, particularly in challenging noisy scenarios. Full article

Understanding how sustainable urban competitiveness evolves within megaregions has become a central concern in urban and regional studies, particularly under the pressures of carbon neutrality, spatial inequality, and network-driven urbanization. This study develops a multidimensional framework to assess the sustainable competitiveness of cities in the Beijing–Tianjin–Hebei “2 + 36” urban agglomeration and examines its spatio-temporal evolution and relational structure. Using a 30-indicator system grounded in factor foundations, economic performance, innovation capacity, openness, and environmental livability, we construct a composite competitiveness index through principal component analysis (PCA). Kernel density estimation reveals a pattern of overall improvement accompanied by widening disparities, characterized by selective agglomeration and the emergence of a pronounced high-value tail. Spatial autocorrelation consistently indicates significant spatial dependence, while LISA analysis identifies persistent low–low clusters and limited spillover absorption around core cities. A modified gravity model further uncovers a transition from a linear, corridor-based linkage structure to a more polycentric and networked competitiveness system, albeit with enduring peripheral weak nodes. The study contributes theoretically by conceptualizing sustainable urban competitiveness as a multidimensional nexus shaped jointly by territorial attributes and relational network structures. It demonstrates that competitiveness dynamics in megaregions emerge from the interplay of hierarchical consolidation, spatial divergence, and network reconfiguration—challenging the traditional assumption of simple core-to-periphery diffusion. The findings offer broader global implications, showing that the Beijing–Tianjin–Hebei case mirrors worldwide megaregional patterns, where proximity alone is insufficient to ensure functional integration, and where coordinated governance, network embeddedness and sustainability transitions increasingly determine regional competitiveness. This research provides a comprehensive analytical foundation for understanding and governing megaregional competitiveness in the era of sustainable development. Full article