Search Results (116)

Ultra-high dimensional longitudinal data feature screening procedures are widely studied, but most require model assumptions. The screening performance of these methods may not be excellent if we specify an incorrect model. To resolve the above problem, a new model-free method is introduced where feature screening is performed by sample splitting and data aggregation. Distance correlation is used to measure the association at each time point separately, while longitudinal correlation is modeled by a specific cumulative distribution function to achieve efficiency. In addition, we extend this new method to handle situations where the predictors are correlated. Both methods possess excellent asymptotic properties and are capable of handling longitudinal data with unequal numbers of repeated measurements and unequal intervals between repeated measurement time points. Compared to other model-free methods, the two new methods are relatively insensitive to within-subject correlation, and they can help reduce the computational burden when applied to longitudinal data. Finally, we use some simulated and empirical examples to show that both new methods have better screening performance. Full article

In Low Earth Orbit (LEO) satellite communication systems, providing reliable data transmission for ultra-high-speed mobile terminals faces severe challenges from dramatic Doppler effects and fast time-varying channels. Orthogonal Time Frequency Space (OTFS) modulation is a promising technique for high-mobility Low Earth Orbit (LEO) satellite communications, but its performance is often limited by inaccurate Channel State Information (CSI) prediction and suboptimal resource allocation, particularly in dynamic channels with coupled parameters like SNR, Doppler, and delay. To address these limitations, this paper proposes an adaptive OTFS frame configuration scheme based on multi-domain channel prediction. We utilize a Long Short-Term Memory (LSTM) network to jointly predict multi-dimensional channel parameters by leveraging their temporal correlations. Based on these predictions, the OTFS transmitter performs two key optimizations: dynamically adjusting the pilot guard bands in the Delay-Doppler domain to reallocate guard resources to data symbols, thereby improving spectral efficiency while maintaining channel estimation accuracy; and performing optimal power allocation based on predicted sub-channel SNRs to minimize the system’s Bit Error Rate (BER). The simulation results show that our proposed scheme reduces the required SNR for a BER of $1\times {10}^{-3}$ by approximately 1.5 dB and improves spectral efficiency by 10.5% compared to baseline methods, demonstrating its robustness and superiority in high-mobility satellite communication scenarios. Full article

To promote the development of long-distance high-speed underwater optical wireless communication (UWOC) based on visible light, this study proposes a high-bandwidth UWOC system based on micro-light-emitting-diodes (micro-LEDs) adopting the Non-Return-to-Zero On-Off Keying (NRZ-OOK) modulation. The numerical simulations reveal that optimizing the structural parameters of gallium nitride (GaN)-based micro-LED through dimensional scaling and quantum well layer reduction may significantly enhance optoelectronic performance, including modulation bandwidth and luminous efficiency. Moreover, experimental validation demonstrated maximum real-time data rates of 420 Mbps, 290 Mbps, and 250 Mbps at underwater distances of 2.3 m, 6.9 m, and 11.5 m, respectively. Furthermore, the underwater audio communication was successfully implemented at an 11.5 m UWOC distance at an ultra-low level of incoming optical power (12.5 µW) at the photodetector (PD) site. The channel characterization yielded a micro-LED-specific attenuation coefficient of 0.56 dB/m, while parametric analysis revealed wavelength-dependent degradation patterns, exhibiting positive correlations between both attenuation coefficient and bit error rate (BER) with operational wavelength. This study provides valuable insights for optimizing underwater optical systems to enhance real-time environmental monitoring capabilities and strengthen security protocols for subaquatic military communications in the future. Full article

Streak cameras, essential for ultrahigh temporal resolution diagnostics in laser-driven inertial confinement fusion, underpin the streak tube imaging LiDAR (STIL) system—a flash LiDAR technology offering high spatiotemporal resolution, precise ranging, enhanced sensitivity, and wide field of view. This study establishes a theoretical model of the STIL system, with numerical simulations predicting limits of temporal and spatial resolutions of ~6 ps and 22.8 lp/mm, respectively. Dynamic simulations of laser backscatter signals from targets at varying depths demonstrate an optimal distance reconstruction accuracy of 98%. An experimental STIL platform was developed, with the key parameters calibrated as follows: scanning speed (16.78 ps/pixel), temporal resolution (14.47 ps), and central cathode spatial resolution (20 lp/mm). The system achieved target imaging through streak camera detection of azimuth-resolved intensity profiles, generating raw streak images. Feature extraction and neural network-based three-dimensional (3D) reconstruction algorithms enabled target reconstruction from the time-of-flight data of short laser pulses, achieving a minimum distance reconstruction error of 3.57%. Experimental results validate the capability of the system to detect fast, low-intensity optical signals while acquiring target range information, ultimately achieving high-frame-rate, high-resolution 3D imaging. These advancements position STIL technology as a promising solution for applications that require micron-scale depth discrimination under dynamic conditions. Full article

The thermal, thermomechanical, and viscoelastic properties of continuous unidirectional (UD) glass fiber/high-density polyethylene (GF/HDPE) and ultra-high-molecular-weight polyethylene/high-density polyethylene (UHMWPE/HDPE) tapes are characterized in this paper in order to support their use in extreme environments. Unlike prior studies that focus on short-fiber composites or limited thermal conditions, this work examines continuous fiber architectures under five operational environments derived from Army Regulation 70-38, reflecting realistic defense-relevant extremes. Differential scanning calorimetry (DSC) was used to identify melting transitions for GF/HDPE and UHMWPE/HDPE, which guided the selection of test conditions for thermomechanical analysis (TMA) and dynamic mechanical analysis (DMA). TMA revealed anisotropic thermal expansion consistent with fiber orientation, while DMA, via strain sweep, temperature ramp, frequency sweep, and stress relaxation, quantified their temperature- and time-dependent viscoelastic behavior. The frequency-dependent storage modulus highlighted multiple resonant modes, and stress relaxation data were fitted with high accuracy (R² > 0.99) to viscoelastic models, yielding model parameters that can be used for predictive simulations of time-dependent material behavior. A comparative analysis between the two material systems showed that UHMWPE/HDPE offers enhanced unidirectional stiffness and better low-temperature performance. At the same time, GF/HDPE exhibits lower thermal expansion, better transverse stiffness, and greater stability at elevated temperatures. These differences highlight the impact of fiber type on thermal and mechanical responses, informing material selection for applications that require directional load-bearing or dimensional control under thermal cycling. By integrating thermal and viscoelastic characterization across realistic operational profiles, this study provides a foundational dataset for the application of continuous fiber thermoplastic tapes in structural components exposed to harsh thermal and mechanical conditions. Full article

The advancement of high-throughput sequencing has revolutionised genomic research by generating large amounts of data. However, Whole-Genome Sequencing is associated with a statistical challenge known as the p >> n problem. We classified 1825 individuals into five breeds based on 11,915,233 SNPs. First, three feature selection algorithms were applied: SNP-tagging and two approaches based on supervised rank aggregation, followed by either one-dimensional (1D-SRA) or multidimensional (MD-SRA) feature clustering. Individuals were then classified into breeds using a deep learning classifier composed of Convolutional Neural Networks. SNPs selected by SNP-tagging yielded the least satisfactory F1-score (86.87%); however, this approach offered rapid computing time. The 1D-SRA was less suitable for ultra-high-dimensional data due to computational, memory, and storage limitations. However, the SNP set selected by this algorithm provided the best classification quality (96.81%). MD-SRA provided a good balance between classification quality (95.12%) and computational efficiency (17x lower analysis time, 14x lower data storage). Unlike SNP-tagging, SRA-based approaches are universal and are not limited to genomic data. This study addressed the demand for efficient computational and statistical tools for feature selection in high-dimensional genomic data. The results demonstrate that the proposed MD-SRA is suitable for the classification of high-dimensional data. Full article

Background: Atrial fibrosis is a key contributor to atrial cardiomyopathy and can be assessed invasively using mean left atrial voltage (MLAV) from electroanatomical mapping. However, the invasive nature of this procedure limits its clinical applicability. Machine learning (ML), particularly regression tree-based models, may offer a non-invasive approach for predicting MLAV using clinical and echocardiographic data, improving non-invasive atrial fibrosis characterisation beyond current dichotomous classifications. Methods: We prospectively included and followed 113 patients with paroxysmal or persistent atrial fibrillation (AF) undergoing pulmonary vein isolation (PVI) with ultra-high-density voltage mapping (uHDvM), from whom MLAV was estimated. Standardised two-dimensional transthoracic echocardiography was performed before ablation, and clinical and echocardiographic variables were analysed. A regression tree model was constructed using the Classification and Regression Trees—CART-algorithm to identify key predictors of MLAV. Results: The regression tree model exhibited moderate predictive accuracy (R² = 0.63; 95% CI: 0.55–0.71; root mean squared error = 0.90; 95% CI: 0.82–0.98), with indexed minimum LA volume and passive emptying fraction emerging as the most influential variables. No significant differences in AF recurrence-free survival were found among MLAV tertiles or model-based generated groups (log-rank p = 0.319 and p = 0.126, respectively). Conclusions: We present a novel ML-based regression tree model for non-invasive prediction of MLAV, identifying minimum LA volume and passive emptying fraction as the most significant predictors. This model offers an accessible, non-invasive tool for refining atrial cardiomyopathy characterisation by reflecting the fibrotic substrate as a continuum, a crucial advancement over existing dichotomous approaches to guide tailored therapeutic strategies. Full article

Terahertz (THz) wireless communications can support ultra-high data rates and secure wireless links with miniaturized devices for unmanned aerial vehicle (UAV) communications. In this paper, a three-dimensional (3D) non-stationary geometry-based stochastic channel model (GSCM) is proposed for multiple-input multiple-output (MIMO) communication links between the UAVs in the THz band. The proposed channel model considers not only the 3D scattering and reflection scenarios (i.e., reflection and scattering fading) but also the atmospheric molecule absorption attenuation, arbitrary 3D trajectory, and antenna arrays of both terminals. In addition, the statistical properties of the proposed GSCM (i.e., the time auto-correlation function (T-ACF), space cross-correlation function (S-CCF), and Doppler power spectrum density (DPSD)) are derived and analyzed under several important UAV-related parameters and different carrier frequencies, including millimeter wave (mmWave) and THz bands. Finally, the good agreement between the simulated results and corresponding theoretical ones demonstrates the correctness of the proposed GSCM, and some useful observations are provided for the system design and performance evaluation of UAV-based air-to-air (A2A) THz-MIMO wireless communications. Full article

A metasurface capable of flexibly manipulating electromagnetic waves to realize holograms presents significant potential in millimeter-wave imaging systems and data storage domains. In this study, full-space three-dimensional holograms are realized from a reflection–transmission integrated reconfigurable metasurface, which can achieve nearly 360° phase coverage in reflection space and 180° phase coverage in transmission space. By adjusting the voltage applied to the constituting electronically tunable meta-atoms of the metasurface, an octahedron hologram constituted by three hologram images in different focal planes is generated in the reflection space at 6.25 GHz. Moreover, a diamond hologram, also composed of three hologram images in different focal planes, is achieved in the transmission space at 6.75 GHz. Both the numerical simulation and experimental measurement are performed to validate the full-space holograms implemented by the modified weighted Gerchberg–Saxton (WGS) algorithm with specific phase distribution in different imaging planes. The obtained results pave the way for a wide range of new applications, such as next-generation three-dimensional displays for immersive viewing experiences, high-capacity optical communication systems with enhanced data encoding capabilities, and ultra-secure anti-counterfeiting solutions that are extremely difficult to replicate. Full article

Maximizing the power output of wind farms is critical for improving the economic viability and grid integration of renewable energy. Active wake control (AWC) strategies, such as yaw-based wake steering, offer significant potential for power generation increase but require predictive models that are both accurate and computationally efficient for real-time implementation. This paper proposes a data-driven observer to rapidly estimate the potential power gain achievable through AWC as a function of the ambient wind direction. The approach is rooted in Koopman operator theory, which allows a linear representation of nonlinear dynamics. Specifically, a model is developed using an Input–Output Extended Dynamic Mode Decomposition framework combined with Sparse Identification (IOEDMDSINDy). This method lifts the low-dimensional wind direction input into a high-dimensional space of observable functions and then employs iterative sparse regression to identify a minimal, interpretable linear model in this lifted space. By training on offline simulation data, the resulting observer serves as an ultra-fast surrogate model, capable of providing instantaneous predictions to inform online control decisions. The methodology is demonstrated and its performance is validated using two case studies: a 9-turbine and a 20-turbine wind farm. The results show that the observer accurately captures the complex, nonlinear relationship between wind direction and power gain, significantly outperforming simpler models. This work provides a key enabling technology for advanced, real-time wind farm control systems. Full article

With the rapid development of wearable electronics, traditional rigid thermal management materials face limitations in flexibility, conformability, and multi-physics adaptability. Low-dimensional carbon materials such as graphene and carbon nanotubes combine ultrahigh thermal conductivity with outstanding mechanical compliance, making them promising building blocks for flexible thermal regulation. This review summarizes recent advances in integrating these materials into textile architectures, mapping the evolution of this emerging field. Key topics include phonon-dominated heat transfer mechanisms, strategies for modulating interfacial thermal resistance, and dimensional effects across scales; beyond these intrinsic factors, hierarchical textile configurations further tailor macroscopic performance. We highlight how one-dimensional fiber bundles, two-dimensional woven fabrics, and three-dimensional porous networks construct multi-directional thermal pathways while enhancing porosity and stress tolerance. As for practical applications, the performance of carbon-based textiles in wearable systems, flexible electronic packaging, and thermal coatings is also critically assessed. Current obstacles—namely limited manufacturing scalability, interfacial mismatches, and thermal performance degradation under repeated deformation—are analyzed. To overcome these challenges, future studies should prioritize the co-design of structural and thermo-mechanical properties, the integration of multiple functionalities, and optimization guided by data-driven approaches. This review thus lays a solid foundation for advancing carbon-based smart textiles toward next-generation flexible thermal management technologies. Full article

Two-dimensional (2D) material-based resistive random-access memory (RRAM) has emerged as a promising solution for neuromorphic computing and computing-in-memory architectures. Compared to conventional metal-oxide-based RRAM, the novel 2D material-based RRAM devices demonstrate lower power consumption, higher integration density, and reduced performance variability, benefiting from their atomic-scale thickness and ultra-flat surfaces. Remarkably, 2D layered metal oxides retain these advantages while preserving the merits of traditional metal oxides, including their low cost and high environmental stability. Through a multi-step dry transfer process, we fabricated a Pd-MoO₃-Ag RRAM device featuring 2D α-MoO₃ as the resistive switching layer, with Pd and Ag serving as inert and active electrodes, respectively. Resistive switching tests revealed an excellent operational stability, low write voltage (~0.5 V), high switching ratio (>10⁶), and multi-bit storage capability (≥3 bits). Nevertheless, the device exhibited a limited retention time (~2000 s). To overcome this limitation, we developed a Gr-MoO₃-Ag heterostructure by substituting the Pd electrode with graphene (Gr). This modification achieved a fivefold improvement in the retention time (>10⁴ s). These findings demonstrate that by controlling the type and thickness of 2D materials and resistive switching layers, RRAM devices with both high On/Off ratios and long-term data retention may be developed. Full article

Non-intentionally added substances (NIAS) in plastic food contact materials represent a critical undercharacterized chemical safety concern, caused by their inherent diversity, potential toxicity, and regulatory challenges. This review synthesizes recent advances and persistent gaps in NIAS analysis, with a primary focus on analytical workflows for non-targeted analysis, alongside a consideration of risk assessment and toxicological prioritization frameworks. Conventional plastics (e.g., polyethylene, polypropylene, or polyethylene terephthalate) as well as emerging materials (e.g., bioplastics and recycled polymers) exhibit different NIAS profiles, including oligomers, degradation products, additives, and contaminants, requiring specific approaches for migration testing, extraction, and detection. Advanced techniques, such as ultra-high-performance liquid chromatography or two-dimensional gas chromatography coupled with high-resolution mass spectrometry, have enabled non-targeted analysis approaches. However, the field remains constrained by spectral library gaps, limited reference standards, and inconsistent data processing protocols, resulting in heavy reliance on tentative identifications. Risk assessment procedures mainly employ the Threshold of Toxicological Concern and classification by Cramer’s rules. Nevertheless, addressing genotoxicity, mixture effects, and novel hazards from recycled or bio-based polymers remains challenging with these approaches. Future priorities and efforts may include expanding spectral databases, harmonizing analytical protocols, and integrating in vitro bioassays with computational toxicology to refine hazard characterization. Full article

The accurate prediction of autonomous vessel CO₂ emissions is critical for achieving IMO 2050 carbon neutrality and optimizing low-carbon maritime operations. Traditional models face limitations in real-time multi-source data analysis and dynamic cross-variable dependency modeling, hindering data-driven decision-making for sustainable autonomous shipping. This study proposes a Multi-scale Channel-aligned Transformer (MCAT) model, integrated with a 5G–satellite–IoT communication architecture, to address these challenges. The MCAT model employs multi-scale token reconstruction and a dual-level attention mechanism, effectively capturing spatiotemporal dependencies in heterogeneous data streams (AIS, sensors, weather) while suppressing high-frequency noise. To enable seamless data collaboration, a hybrid transmission framework combining satellite (Inmarsat/Iridium), 5G URLLC slicing, and industrial Ethernet is designed, achieving ultra-low latency (10 ms) and nanosecond-level synchronization via IEEE 1588v2. Validated on a 22-dimensional real autonomous vessel dataset, MCAT reduces prediction errors by 12.5% MAE and 24% MSE compared to state-of-the-art methods, demonstrating superior robustness under noisy scenarios. Furthermore, the proposed architecture supports smart autonomous shipping solutions by providing demonstrably interpretable emission insights through its dual-level attention mechanism (visualized via attention maps) for route optimization, fuel efficiency enhancement, and compliance with CII regulations. This research bridges AI-driven predictive analytics with green autonomous shipping technologies, offering a scalable framework for digitalized and sustainable maritime operations. Full article

As sixth-generation mobile communication (6G) advances towards ultra-high speed and global coverage, visible light communication (VLC) has emerged as a crucial complementary technology due to its ultra-high bandwidth, low power consumption, and immunity to electromagnetic interference. Modulation format recognition (MFR) plays a vital role in the dynamic optimization and adaptive transmission of VLC systems, significantly influencing communication performance in complex channel environments. This paper systematically reviews the research progress in MFR for VLC, comparing the theoretical frameworks and limitations of traditional likelihood-based (LB) and feature-based (FB) methods. It also explores the advancements brought by deep learning (DL) technology, particularly in enhancing noise robustness, classification accuracy, and cross-scenario adaptability through automatic feature extraction and nonlinear mapping. The findings indicate that DL-based MFR substantially enhances recognition performance in intricate channels via multi-dimensional feature fusion, lightweight architectures, and meta-learning paradigms. Nonetheless, challenges remain, including high model complexity and a strong reliance on labeled data. Future research should prioritize multi-domain feature fusion, interdisciplinary collaboration, and hardware–algorithm co-optimization to develop lightweight, high-precision, and real-time MFR technologies that align with the 6G vision of space–air–ground–sea integrated networks. Full article