Search Results (60)

Sixth-generation (6G) vehicular systems are expected to provide extreme reliability, ultra-low latency, and high data rates while simultaneously enabling accurate and timely perception of vehicles, road users, and surrounding environments. Conventional vehicle-to-everything (V2X) systems treat communication and environmental sensing separately and therefore fall short in dynamic, dense, and safety-critical driving scenarios. Integrated sensing and communication for V2X (ISAC-V2X) addresses this limitation by sharing hardware and spectrum for dual-functional operation. This survey consolidates the state of the art across eight thematic areas: fundamentals and taxonomy; vehicular channel models and sensing metrics; physical-layer techniques, including waveform design, beamforming, reconfigurable intelligent surfaces, non-orthogonal multiple access, and rate-splitting multiple access; security and privacy; networked and cell-free ISAC with multi-access edge computing; multi-modal perception using radar, LiDAR, camera, and radio-frequency data; artificial intelligence and ML for channel, beam, target, and sensing-data processing; and future research directions. Five consolidated research gaps are synthesized from the reviewed literature: end-to-end frameworks for networked ISAC-V2X under high mobility; unified multi-modal AI-native architectures for joint sensing, communication, localization, and security; incomplete standardization and interoperability; limited validation, testbeds, and reproducible benchmarks; and scalability, robustness, and cross-domain optimization. This survey concludes with standardization and testbed recommendations and presents a roadmap toward deployable 6G ISAC-V2X systems. Full article

Visible foliar damage in maize caused by fall armyworm(Spodoptera frugiperda)is commonly assessed through manual field scouting, a labor-intensive process that is difficult to scale across large planting areas. Multispectral and hyperspectral sensing can support more objective assessment but remain costly and impractical for routine field use. This exploratory case study evaluated whether low-cost red–green–blue (RGB) imagery can provide preliminary indicators of visible foliar damage associated with natural S. frugiperda infestation. RGB video was recorded in maize fields in Jerez, Zacatecas, Mexico, yielding seven field-acquired sequences and 302 extracted frames. The pipeline combined hue–saturation–value (HSV)-based foliar segmentation with four visible-spectrum indices—Excess Green (ExG), Excess Red (ExR), the Visible Atmospherically Resistant Index (VARI), and the Green Leaf Index (GLI)—an ExG-ratio damage threshold, and a 17-feature descriptor per frame used to train a Random Forest (RF) severity classifier. The study is positioned relative to RGB, Unmanned Aerial Vehicle (UAV)-based, deep learning, and multimodal approaches through its emphasis on traceability, low acquisition cost, and sequence-aware validation. Using the recovered canonical HSV/ExG-ratio pipeline, sequence-level mean damage ranged from 0.1086% to 0.4511%, with maximum frame-level damage up to 7.7401%. Severity labels were percentile-derived from the canonical damage index, yielding 100 Low, 99 Medium, and 103 High samples. Under a stratified frame-level split, the RF baseline reached 76.9% accuracy and a macro F1-score of 0.759. Under leave-one-sequence-out validation, performance decreased to 57.3% overall accuracy and 0.577 macro F1-score, indicating sequence-level dependence and supporting a conservative interpretation of classifier generalization. A zero-shot comparison using the Segment Anything Model (SAM) on a curated ten-frame-per-sequence subset produced higher damage estimates (SAM 1.30–11.67% versus HSV 0.82–2.61% on the same frames), suggesting HSV segmentation may under-detect pale or bleached tissue. These results provide preliminary, exploratory evidence that low-cost RGB indices can capture information associated with visible foliar damage in the studied recordings, without establishing agronomic validation, generalization beyond this dataset, or readiness for field deployment. Full article

Large integrated station–bridge buildings combine track-bearing members, station floors, transfer structures, columns, and urban-rail facilities within a single coupled structural system. For such buildings, refined train–track–station dynamic simulations can reproduce train-induced vibration, but repeated time-history analysis remains costly when many operating conditions must be screened. This study develops a simulation-based response-database framework for multi-horizon forecasting of front-end carbody vertical acceleration (FCVA), defined here as the vertical acceleration at the front-end floor evaluation point of the leading carbody, in the integrated station–bridge building of Yichang North Railway Station. The project-specific database contains 700 operating cases constructed from 100 Latin-hypercube-sampled combinations of a dimensionless track-spectrum amplitude multiplier (TSA), structural damping ratio (DR), and track-spectrum initial moving position (TSIP), each evaluated at seven train speeds. With a sampling interval of 0.002 s, supervised samples were constructed using a 200-point historical window, and prediction horizons from 20 to 300 steps (0.04–0.60 s) were evaluated under a case-level split. Classical regression, tree ensembles, a multilayer perceptron, recurrent networks, a temporal convolutional network, and a Transformer were compared after automated hyperparameter selection. For the 20-step task, Extra Trees achieved the best performance, with a root mean squared error (RMSE) of 0.00336 m/s² and $R^2$ = 0.9958. In the independently refitted reference-fixed horizon experiment, Extra Trees retained $R^2$ = 0.9526 at the 300-step horizon, while the temporal convolutional network (TCN) RMSE increased from 0.00394 to 0.01502 m/s². The results show that the response database preserves exploitable short- to medium-range dynamic continuity, although phase drift and peak-timing uncertainty increase as the forecast horizon becomes longer. Parameter analysis indicates that train speed dominates both response energy and forecast error, whereas TSA mainly affects amplitude-related response metrics. On a common central processing unit (CPU) platform, the saved Extra Trees model processed 10,000 held-out windows in $0.1404\pm 0.0008$ s. The proposed method provides a computationally efficient response-screening and post-processing layer for design-stage assessment and operating-scenario comparison within the modeled parameter domain, complementing rather than replacing refined dynamic simulation and field validation. Full article

Children with autism spectrum disorder (ASD) often exhibit atypical patterns of visual attention allocation and social-cue processing. Eye-tracking scanpath (ETSP) retains information about fixation points, saccade paths and their temporal changes in the form of images, providing an intuitive and computable data representation for analyzing ASD-related visual attention patterns. However, in ASD auxiliary identification studies, the same participant often generates multiple eye-tracking recordings or multiple visual representation samples. If participant independence is not properly considered during model evaluation, the training and test sets may share individualized eye-movement patterns from the same child. In such cases, the model may learn subject-specific characteristics rather than stable and transferable ASD-related visual attention features, leading to an overestimation of its recognition ability on unseen participants. To address this issue, we propose a Global–Local Collaborative Fusion Network (GLCF-Net) under a strict participant-independent splitting protocol. Specifically, the proposed method first maps ETSP images into patch token sequences through a shared Patch Embedding layer. A CNN-based local branch is then used to extract local trajectory morphology, path density, and spatial neighborhood structure, while a ViT-based global branch models cross-region gaze transitions and the overall attention distribution. Finally, a gated adaptive fusion module dynamically integrates local and global information to enhance the representation of stable visual attention features. In the primary repeated stratified five-fold participant-level evaluation, averaging the two out-of-fold probabilities for each participant yielded an Accuracy of 87.0% and a ROC-AUC of 93.7%; the original participant split, retained as a secondary analysis, yielded an Accuracy of 83.52% and a ROC-AUC of 90.27%. Under the reported frozen-backbone configurations, the model also showed a balanced pattern across Accuracy, Recall, and F1-score. These results characterize performance for unseen participants within the same dataset and acquisition conditions. Full article

Quantifying the direction, strength and temporal clustering of cross-border financial risk contagion calls for methods that go beyond linear correlation. We suggest a two-layer framework that brings together transfer entropy and a multivariate Hawkes self-exciting point process on a time-varying, directed, weighted network. In the first layer, one-to-one transfer entropies of sovereign credit default swap spreads are estimated with a bias-corrected k nearest neighbour estimator, and this step detects nonlinear and directional information transfer between spreads. The second layer is a multivariate Hawkes process that models how extreme loss events arrive and mutually excite one another across countries, and it gives an excitation intensity matrix, encoding the way a tail event in one country raises the likelihood of an instantaneous hazard occurring in another. By merging these two layers, we obtain a composite, directed, weighted adjacency matrix in which the weights of the edges reflect both information flow and event clustering. We introduce a network-level contagion intensity index and split it into direct, indirect and feedback terms using the graph Laplacian spectrum. Von Neumann graph entropy together with the spectral gap ratio serve as entropy-based measures of the complexity and fragility of the evolving network. We validate the choice of Shannon-type entropy through a Tsallis q-sensitivity analysis, and we verify the nonlinear dependence structure of the data using BDS tests and maximal Lyapunov exponent estimates. Three empirical findings emerge from analysing 20 sovereign CDS markets from January 2015 to December 2025: (i) directional risk spillover signals derived based on transfer entropy are more timely than those derived from variance decomposition; (ii) the Hawkes excitation component amplifies measured contagion intensity by 35 to 58 percent during the COVID-19 shock and the 2022 European energy crisis relative to a transfer-entropy-only baseline; (iii) von Neumann graph entropy reaches historically extreme values 7 to 12 trading days before the peak drawdown in a Global Sovereign Bond Index. These results hold across rolling window lengths, significance thresholds, alternative entropy functionals and alternative Hawkes kernels. Full article

Accurate estimation of Alzheimer’s disease (AD) severity from structural magnetic resonance imaging (MRI) remains difficult, as disease-associated anatomical alterations are often subtle and publicly available datasets are typically too small to support robust deep learning model training. This feasibility study sought to determine how much Alzheimer’s disease spectrum-related information could be extracted from a small structural MRI cohort using a deliberately lightweight two-dimensional convolutional neural network (2D CNN), and whether transfer learning improves model performance. This study was intended as a methodological proof of concept rather than the development of a clinically deployable diagnostic tool. Structural scans and Clinical Dementia Rating (CDR) labels from the OASIS-1 dataset were filtered to 214 subjects: 124 cognitively normal (CN), 65 with mild cognitive impairment (MCI; CDR = 0.5), and 25 with AD-level impairment (CDR ≥ 1). A compact 2D CNN trained from scratch and a transfer learning model (frozen ImageNet MobileNetV2 features) were evaluated on four binary tasks (CN vs. AD, MCI vs. AD, CN vs. MCI, and CN vs. any impairment) under identical pre-processing and subject-level repeated 5-fold cross-validation (10 repeats), with the decision threshold tuned only on an inner split. Discrimination was summarized by ROC-AUC with 95% confidence intervals (CIs), permutation tests against chance, and per-task sensitivity and specificity. The from-scratch CNN recovered only a broad normal-versus-impaired signal (CN vs. any impairment AUC 0.59) and was at chance on adjacent-stage tasks (MCI vs. AD 0.41; CN vs. MCI 0.51). Transfer learning improved every task: CN vs. AD AUC 0.745 (95% CI 0.730–0.763), CN vs. any impairment 0.642, CN vs. MCI 0.601, and MCI vs. AD 0.599. On an independent OASIS-2 cohort, the transfer learning CN vs. AD model retained AUC 0.748. In this small-data regime, transfer learning recovers substantially more Alzheimer-spectrum signals than a from-scratch CNN, but performance remains modest because it is bounded by CDR-based, non-biomarker-confirmed labels, suggesting the model separates CDR-defined cognitive-status groups rather than detecting AD pathology. Full article

Background/Objectives: The early identification of skin cancer by standard RGB dermoscopy is a clinical difficulty because of the complex visual differences between impacted lesions and healthy tissue. Methods: For the biomedical challenge, a novel approach to signal processing and image reconstruction is introduced in this study, called the spectrum-aided visual enhancer (SAVE). The proposed SAVE mechanism aims at reconstructing the diagnostically relevant spectral information from the conventional RGB dermoscopic images using the principles of hyperspectral imaging (HSI) and band selection (BS). After quality control and pre-processing, the images in the ISIC2019 dataset were selected, with 865 images that contain basal cell carcinoma (BCC), seborrheic keratosis (SK), and actinic keratosis (AK) lesions. To reduce data leakage, the dataset was split into training, validation, and testing subsets of 70%, 20%, and 10%, respectively. Five supervised deep learning object detection models were trained and tested on the conventional RGB image dataset and on the SAVE-enhanced dataset. Five supervised deep learning object detection models, namely, YOLOv8, YOLOv10, YOLOv11, SSDLite, and SSD, were trained and tested on the conventional RGB image dataset and the SAVE-enhanced dataset. Additional repeated experimental assessments and statistical comparisons were also carried out to evaluate the improvement in performance. Results: The experimental results showed that the SAVE-based pre-processing always yielded better performance in terms of lesion detection than conventional RGB image processing. The SAVE framework for SSD was evaluated and compared with all other evaluated models and was found to be the most successful, with an accuracy of 96%, a precision of 97%, a recall of 96%, and an F1 score of 96%. Conclusions: The results indicate that the proposed SAVE framework could be a promising RGB-compatible spectral enhancement technique for boosting skin cancer detection and computer-aided dermatologic analysis with the aid of AI. Full article

We present a high-precision theoretical study of attosecond transient absorption spectroscopy (ATAS) of atomic hydrogen by numerically solving the time-dependent Schrödinger Equation (TDSE). A broadband extreme ultraviolet (XUV) attosecond pulse creates a wave packet of singly-excited bound states, which is subsequently probed by a time-delayed synthesized optical attosecond pulse (SOAP) with varying bandwidths and durations. When the SOAP has a narrow bandwidth (1.3–1.5 eV) and a long duration (~17 fs), the absorption spectrum exhibits conventional features, namely AC Stark shifts, half-cycle modulations (1.48 fs), and light-induced intermediate states, consistent with previous ATAS studies. In contrast, when the SOAP has a broad bandwidth (0.5–5.5 eV) and an attosecond duration (400 as), the dynamics are completely different. The spectrum reveals transverse wavelike modulations along the absorption lines and, remarkably, quantum beats with distinct frequencies, which are different from previous reports in hydrogen ATAS. To interpret these observations, we employ a dipole-control model. The model quantitatively reproduces the dominant modulation frequencies, identifying resonant couplings via two-photon processes (TPPs, 1.89 eV, period 2.18 fs) and three-photon processes (THPPs, 10.2 eV and 12.1 eV), as well as higher-order couplings. The validity of the $\delta$-like pulse approximation is quantitatively assessed. The model remains accurate for pulse durations shorter than 700 as (bandwidth broader than 3.5 eV) but fails for longer pulses (exceeding 4 fs), where energy level splittings emerge. Our results demonstrate that the dipole-control model provides a reliable and intuitive framework for interpreting complex multiphoton interactions in ATAS, and highlight the unique capability of broadband SOAP probes to resolve attosecond-scale quantum beats inaccessible with conventional few-cycle infrared pulses. 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

The present study focuses on liquid-precursor-mediated chemical vapor deposition (under ambient pressure and moderate temperature range) of WSe₂ on sapphire using ammonium meta-tungstate and sodium cholate. The investigation provides additional results and information for the WSe₂ cluster formations on sapphire as an extension of our previous study, especially based on structural, chemical and morphological characterization of the observed largest and predominant polygonal WSe₂ domains whose lateral size can reach several hundreds of micrometers. In addition, highly symmetrical shapes were also observed. The Raman spectroscopy and atomic force microscopy identified the formation of both mono- and multilayered WSe₂. Moreover, the Raman spectrum analysis shows a complex peak structure with unusual splitting effects in the second-order modes marking strong activity of excitonic-resonance processes. Full article

Chirped fiber Bragg gratings (CFBGs) are robust diagnostic sensors that are widely used to track detonation-driven and shock wave propagation. CFBGs are inscribed with a linearly chirped periodic index of refraction changes that alter the Bragg wavelength along the length of the probe. The light return of each individual Bragg element is captured by a detector at a unique time to map the full reflected spectrum. The CFBG spectrum is measured with a dispersive Fourier transform of the reflected light that temporally stretches the spectrum to increase spatial resolution and make a one-to-one map of the wavelength on a time axis. Here, we propose an improvement of CFBG temporal resolution by incorporating two co-linear laser pulses with orthogonal polarization states and a 5 ns time offset. The two separate signals were split and tracked by two separate detectors. An oscilloscope captured good separation in the signals, and two separate spectrograms were generated and interleaved in the post-processing of the data. This novel technique doubled the CFBG temporal resolution and led to a doubled location resolution. As a proof-of-concept of this technique, the resolution improvement was compared between standard CFBG measurements and the two polarization states method on a position-sensitive CFBG sensor. CFBG resolution doubling will advance sensor capabilities and will have a direct impact on improving capture and analysis in dynamic, high-explosive experiments. Full article

Background: Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by heterogeneous behavioral and neurological patterns, complicating timely and accurate diagnosis. Behavioral datasets are commonly used to diagnose ASD. In clinical practice, it is difficult to identify ASD because of the complexity of the behavioral symptoms, overlap of neurological disorders, and individual heterogeneity. Correct and timely identification is dependent on the presence of skilled professionals to perform thorough neurological examinations. Nevertheless, with developments in deep learning techniques, the diagnostic process can be significantly improved by automatically identifying and automatically classifying patterns of ASD-related behaviors and neuroimaging features. Method: This study introduces a novel multimodal diagnostic paradigm that combines structured behavioral phenotypes and structural magnetic resonance imaging (sMRI) into an interpretable and personalized framework. A Generalized Additive Model with Interactions (GAMI-Net) is used to process behavioral data for transparent embedding of clinical phenotypes. Structural brain characteristics are extracted via a hybrid CNN–GNN model, which retains voxel-level patterns and region-based connectivity through the Harvard–Oxford atlas. The embeddings are then fused using an Autoencoder, compressing cross-modal data into a common latent space. A Hyper Network-based MLP classifier produces subject-specific weights to make the final classification. Results: On the held-out test set drawn from the ABIDE-I dataset, a 20% split with about 247 subjects, the constructed system achieved an accuracy of 99.40%, precision of 100%, recall of 98.84%, an F1-score of 99.42%, and an ROC-AUC of 99.99%. For another test of generalizability, five-fold stratified cross-validation on the entire dataset yielded a mean accuracy of 98.56%, an F1-score of 98.61%, precision of 98.13%, recall of 99.12%, and an ROC-AUC of 99.62%. Conclusions: These results suggest that interpretable and personalized multimodal fusion can be useful in aiding practitioners in performing effective and accurate ASD diagnosis. Nevertheless, as the test was performed on stratified cross-validation and a single held-out split, future research should seek to validate the framework on larger, multi-site datasets and different partitioning schemes to guarantee robustness over heterogeneous populations. Full article

Spectrum map fusion has emerged as an effective technique to enhance the accuracy of spectrum map construction. However, many existing fusion methods fail to address the strong correlation between spectrum data, resulting in sub-optimal performance. In this paper, we propose a new multi-unmanned aerial vehicle (UAV) spectrum map fusion method based on differential ridge regression. We first construct spectrum maps of UAVs by using differential features of spectrum data. Next, we present a spectrum map fusion model by leveraging the spatial distribution characteristic of spectrum data. To reduce the sensitivity of the fusion model to the strongly correlated data, a new map fusion regularization term is designed, which introduces $l_2$-norm to constrain the fusion regularization parameters and compress the ridge regression coefficient sizes. As a result, accurate spectrum maps can be constructed for the environments with highly correlated spectrum data. We then formulate a model-driven solution to the spectrum map fusion problem and derive its lower bound. By combining the propagation characteristics of the spectrum signal with the developed Lagrange duality, we can guarantee the convergence of map fusion processing while enhancing the convergence rate. Finally, we propose an accelerated maximally split alternating directions method of multipliers (AMS-ADMM) to reduce the computational complexity of spectrum map construction. Simulation results demonstrate that our proposed method can effectively eliminate external noise interference and outliers, and achieve an accuracy improvement of more than 27% compared to state-of-the-art fusion methods in spectrum map construction with low complexity. Full article

Mouthguards are recommended for all sports that may cause injuries to the head and oral cavity. Custom mouthguards, made conventionally in the thermoforming process from ethylene vinyl acetate (EVA), face challenges with thinning at the incisor area during the process. In contrast, additive manufacturing (AM) processes enable the precise reproduction of the dimensions specified in a computer-aided design (CAD) model. The potential use of filament extrusion materials in the fabrication of custom mouthguards has not yet been explored in comparative studies. Our research aimed to compare five commercially available filaments for the material extrusion (MEX) also known as fused deposition modelling (FDM) of custom mouthguards using a desktop 3D printer. Samples made using Copper 3D PLActive, Spectrum Medical ABS, Braskem Bio EVA, DSM Arnitel ID 2045, and NinjaFlex were compared to EVA Erkoflex, which served as a control sample. The samples underwent tests for ultimate tensile strength (UTS), split Hopkinson pressure bar (SHPB) performance, drop-ball impact, abrasion resistance, absorption, and solubility. The results showed that Copper 3D PLActive and Spectrum Medical ABS had the highest tensile strength. DSM Arnitel ID 2045 had the highest dynamic property performance, measured with the SHPB and drop-ball tests. On the other hand, NinjaFlex exhibited the lowest abrasion resistance and the highest absorption and solubility. DSM Arnitel ID 2045’s absorption and solubility levels were comparable to those of EVA, but had significantly lower abrasion resistance. Ultimately, DSM Arnitel ID 2045 is recommended as the best filament for 3D-printing mouthguards. The properties of this biocompatible material ensure high-impact energy absorption while maintaining low fluid sorption and solubility, supporting its safe intra-oral application for mouthguard fabrication. However, its low abrasion resistance indicated that mouthguards made from this material may need to be replaced more frequently. Full article

This paper presents an artificial intelligence-based model for the classification of maritime vessel images obtained by cameras operating in the visible part of the electromagnetic spectrum. It incorporates both the deep learning techniques for initial image representation and traditional image processing and machine learning methods for subsequent image classification. The presented model is therefore a hybrid approach that uses the Inception v3 deep learning model for the purpose of image vectorization and a combination of SVM, kNN, logistic regression, Naïve Bayes, neural network, and decision tree algorithms for final image classification. The model is trained and tested on a custom dataset consisting of a total of 2915 images of maritime vessels. These images were split into three subsections: training (2444 images), validation (271 images), and testing (200 images). The images themselves encompassed 11 distinctive classes: cargo, container, cruise, fishing, military, passenger, pleasure, sailing, special, tanker, and non-class (objects that can be encountered at sea but do not represent maritime vessels). The presented model accurately classified 86.5% of the images used for training purposes and therefore demonstrated how a relatively straightforward model can still achieve high accuracy and potentially be useful in real-world operational environments aimed at sea surveillance and automatic situational awareness at sea. Full article