Underwater wireless optical communication (UWOC) has broad application prospects in fields such as ocean observation and underwater detection due to its advantages of high bandwidth and low latency. However, signal attenuation caused by absorption and scattering, pointing errors, and turbulence in underwater channels severely affect system reliability. Therefore, accurate modeling of channel characteristics and analysis of bit error rate (BER) performance are key problems in system design. In this paper, an aggregate channel model is proposed for UWOC systems. It comprehensively considers absorption-scattering attenuation and pointing errors. In addition, the hybrid exponential-generalized gamma (EGG) distribution model is adopted to characterize turbulence-induced signal fluctuations. The cumulative distribution function of the aggregate channel transmittance and the closed-form expression for the system's average BER under OOK modulation are derived. Furthermore, a simple closed-form approximation of the average BER for the UWOC is further derived in the high signal-to-noise ratio (SNR) regime. Then a Monte Carlo (MC) simulation platform is established for verification. The results show that the theoretical curve is highly consistent with the MC simulation data points. The attenuation coefficient of water quality determines the maximum reachable distance of the system, and the increase of turbulence intensity will systematically raise the BER curve. This degradation requires additional SNR margin for compensation. The results show that with the increase of transmission distance, the BER performance deteriorates. However, the performance can be improved by increasing the SNR, the transmission power, and the receiving aperture or reducing the beam divergence angle. The proposed model is appropriate for the assessment and optimization of UWOC systems.

Alexithymia has been proposed as a ransdiagnostic vulnerability for behavioral addictions, but its role in problematic Internet-related behaviors (PIB) has not been quantified across phenotypes and populations. We conducted a preregistered systematic review and meta-analysis following Preferred Reporting Items for Systematic reviews and Meta-Analyses. Major databases were searched in October 2025 for studies reporting quantitative associations between alexithymia and PIB (problematic Internet, smartphone, social media, gaming use, or cyberbullying). Risk of bias was appraised with Joanna Briggs Institute tools. Random-effects models pooled correlations and standardized regression coefficients; three-level and robust-variance models accounted for multiple effects per study and examined moderators and adjustment for internalizing symptoms. Eighty-three studies met inclusion criteria; 78 independent samples (N = 42,478; 369 effect sizes total, comprising 302 zero-order correlations and 67 standardized regression coefficients) contributed to the meta-analysis. The pooled correlation between alexithymia and PIB was r = 0.36 (95% confidence interval 0.34-0.38; I[2] = 77.6%). Associations were stronger for addictive-use PIB (r = 0.37) than for cyberbullying (r = 0.29), broadly comparable across world regions, and tended to be larger in adults than adolescents. In multivariable models, coefficients attenuated from about β = 0.37 to β = 0.18 after controlling for depression, anxiety, and other internalizing symptoms, but remained statistically significant. Sensitivity and publication-bias analyses indicated that the association was robust. Alexithymia shows a moderate association with PIB across age groups, partly via shared variance with internalizing psychopathology but also through unique pathways. Longitudinal and mechanistic studies should test whether targeting emotional awareness can help prevent or reduce PIB.

Biofilms are microbial communities enclosed in a matrix that increases antibiotic tolerance and underlies persistent device-associated infections, making label-free, non-destructive characterization important. Established differences in cell-wall structure between Gram-positive and Gram-negative bacteria are expected to alter light scattering, motivating the hypothesis that the two groups differ in optical attenuation. This proof-of-concept study tested that optical prediction using optical coherence tomography. The optical attenuation coefficient was extracted from cross-sectional images. Gram-positive biofilms showed higher attenuation than Gram-negative biofilms, with every cross-group comparison significant, while species within each group were statistically indistinguishable, indicating a Gram-level descriptor. Two deep-learning classifiers separated the groups with over 97% accuracy because the attenuation distributions overlap minimally; this demonstrates the feasibility of automation rather than a requirement for deep learning. The attenuation differences are consistent with the hypothesis presented in the study, suggesting that these structural differences may contribute to the observed optical contrast.

The microwave absorption performance depends on both impedance matching and electromagnetic energy dissipation. Conventional filler-functionalized polyimide (PI) composites can provide strong electromagnetic attenuation, but their limited foamability under supercritical carbon dioxide (scCO2) restricts porous structure formation and thus impairs impedance matching. To address these limitations, this study proposes an intrinsic molecular modification strategy based on a chain segment design. Furthermore, by combining synergistic dielectric-magnetic loss from nanofillers with a double-layer heterogeneous architecture, a PI-based electromagnetic shielding nanocomposite with strong microwave absorption performance and low-reflection characteristics was constructed. Bisphenol A dianhydride (BPADA) was used as the dianhydride monomer, and a series of PI copolymers with tunable chain segment compositions were prepared through the reaction of BPADA with mixed aromatic diisocyanate (MDI) and aliphatic diisocyanate (HDI), followed by thermal imidization/cyclization. The changes in molecular chain mobility, scCO2 foaming behavior, and cellular structure associated with the nominal HDI feed fraction were systematically investigated. The results showed that increasing the nominal HDI feed fraction was associated with an improved rigid-flexible balance of the PI backbone, enhanced segmental mobility, a broader foaming window, and the formation of PI foams with uniform and compact cellular structures. CNT@Fe3O4 nanofillers with both dielectric and magnetic loss capabilities were further introduced to improve the intrinsic electromagnetic loss of the composite foams. The calculated microwave absorption results showed that, after pore structure regulation, the PI/CNT@Fe3O4 nanocomposite foam exhibited markedly enhanced microwave absorption in the X-band. At a porosity of 46 vol %, the minimum reflection loss reached -38 dB at a thickness of 2 mm. A double-layer heterogeneous structure comprising a CNT@Fe3O4-containing porous absorbing layer and a CNT-containing dense reflecting layer was then constructed to exploit impedance gradient transition, dielectric-magnetic synergistic loss, and multiple reflection/reabsorption processes. The optimized sample showed an average absorption coefficient of 0.92 in the X-band and a maximum A/R ratio of 332, indicating pronounced high absorption and low reflection characteristics. This study provides a new strategy for designing and fabricating lightweight, high-performance PI-based electromagnetic protection materials with an absorption-dominated shielding behavior.

The integration of broadband microwave absorption, infrared modulation, and mechanical flexibility within a single material remains a formidable challenge. Herein, a gradient multilayered composite is constructed by sequentially casting condensation-type room-temperature-vulcanized silicone rubber (RTV) layers containing carbonyl iron powder (CIP), conductive carbon black (CB), and zinc sulfide (ZnS) at progressively increasing filler loadings. This architecture spatially decouples impedance matching, electromagnetic attenuation, conductive reflection, and infrared modulation. The gradient composite achieves an absorption coefficient exceeding 0.9 across 8-18 GHz, corresponding to a 10 GHz effective absorption bandwidth. This broadband performance originates from a gradient impedance matching mechanism, whereby the top low-permittivity layer admits microwaves with minimal reflection and the underlying CIP layers progressively attenuate the transmitted energy through magnetic and dielectric losses, while the conductive CB bottom layer redirects residual waves back into the lossy region for a second attenuation pass. The mechanism is verified by transmission matrix method calculations and full-wave electromagnetic simulations. The ZnS-containing layer imparts tunable infrared emissivity, decreasing from 0.781 to 0.695 (3-5 µm) as the ZnS content increases from 10 to 30 wt%, with a steady-state radiation temperature reduction of 22.6 °C relative to Neat RTV. Mechanically, the gradient composite retains an elongation at break of 73.8%, approximately 2.6 times that of the individual brittle functional layers, owing to the protective effect of the ductile outer layers. This gradient multilayered design overcomes the inherent trade-offs among microwave absorption, infrared modulation, and mechanical flexibility, offering a versatile platform for flexible multifunctional composites.

Image dehazing is a basic low-level vision task designed to recover high-fidelity images from hazed images. In autonomous driving, aerial surveillance, and outdoor scene recognition, image quality is significantly degraded by light attenuation due to scattering in the propagation medium, especially under hazy, foggy, or smoggy conditions. While remarkable advances have been made with existing deep learning methods, they remain constrained by over-smoothing, ineffective use of haze frequency, and insufficient consideration of multi-scale feature interactions. To meet these challenges, we introduce a novel single-image dehazing framework, called Low-Frequency Enhanced Feature Transmission Network (LEFTNet), which consists of four special modules: the Haze Suppression Block (HSB), the Dual-Branch Block (DBB), the Cross-Scale Fusion Block (CSFB), and the Channel Mining Mechanism (CMM). In the first stage, a hazy input image is decomposed into two complementary low-frequency representations: a haze-related component (I haze L) and a frequency-domain component (I freq L), which are jointly processed by the HSB for early, channel-selective haze attenuation. Then, the encoder extracts multi-scale hierarchical features, which are cleaned up at each stage by the Channel Mining module. The DBB's residual refinement enables intra-image global-to-local feature interactions, while the CSFB's learnable fusion coefficients enable inter-scale feature fusion. A dual fully connected bottleneck with Global Average Pooling (GAP) suppresses haze-dominant channels while enhancing structure-sensitive channels. Two symmetric branches ensure complementary representation learning, and a hybrid loss function comprising L1 loss and perceptual LSSIM loss assures both pixel-wise accuracy and perceptual fidelity. Comprehensive experiments on four benchmarks, RESIDE-6K, I-Haze, O-Haze, and NH-Haze, demonstrate that LEFTNet achieves competitive performance across the evaluated Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), Average Mean Brightness Error (AMBE), Gradient Magnitude Similarity Deviation (GMSD), and Visual Saliency-Induced Index (VSI) metrics compared with recent dehazing methods.

Climate risk is playing an increasingly important role in shaping corporate behavior and non-financial disclosure. This study examines the nonlinear relationship between corporate climate risk (CR) and public health disclosure (PHD) using panel data from Chinese listed companies over the period 2000-2024.

Ultrasound attenuation coefficient estimation (ACE) has emerged as a quantitative imaging biomarker for noninvasive assessment of hepatic steatosis. A system-independent technique based on spectral normalization, known as the reference frequency method (RFM), was previously proposed to estimate ACE without requiring a well-calibrated reference phantom. Furthermore, incorporating harmonic imaging can significantly suppress reverberation signals. In previous clinical study, RFM has achieved high correlation with MRI-PDFF, demonstrating its potential for clinical application. However, a major challenge of RFM is the presence of oscillations in the frequency power-ratio decay curves (FPDCs), which can distort the linear fitting used to estimate the attenuation coefficient and consequently degrade ACE accuracy. These oscillations arise from constructive and destructive interference among backscattered echoes, resulting in oscillatory fluctuations in the measured power spectrum that propagate into the FPDCs. We propose a transmission scheme combining multiple frequencies and steering angles to mitigate oscillations in the FPDCs. Averaging these FPDCs suppresses the interference-induced oscillations while preserving the attenuation-dependent decay trend, thereby improving linearity and the accuracy of ACE results. In in-vitro experiments using calibrated phantoms (0.5 and 0.76 dB/cm/MHz) demonstrated that the proposed method improved FPDC linearity and ACE accuracy, achieving an R2 of 0.99 and attenuation coefficient estimates of 0.51 and 0.77 dB/cm/MHz, versus an R2 of 0.89 and estimates of 0.56 and 0.70 dB/cm/MHz for conventional RFM. The proposed method also demonstrated superior performance in a pilot patient study (n=15), achieving a stronger correlation with MRI-PDFF (R = 0.89 vs. 0.83) while reducing inter-measurement variability, indicating improved robustness and clinical potential.

Low-energy gamma-ray attenuation measurements are strongly influenced by coherent (Rayleigh) scattering and geometrical effects, particularly under non-ideal broad-beam experimental conditions. In this work, a Monte Carlo-based correction methodology using the MCNP6 code was developed to improve the accuracy of mass attenuation coefficient measurements in 3D-printed polymeric materials. Experimental measurements were performed for acrylonitrile butadiene styrene (ABS), polyethylene terephthalate glycol (PETG), and polylactic acid (PLA) using [241]Am and [137]Cs gamma-ray sources in combination with a 2 × 2″ NaI(Tl) scintillation detector. Monte Carlo simulations were conducted to reproduce both the experimental measurement geometry and an idealized narrow-beam configuration, enabling the determination of correction factors for low-energy attenuation measurements affected by scattering effects. Additional simulations were performed within the 40-100 keV energy range to develop mathematical fitting functions capable of estimating scattering and geometrical corrections as a function of photon energy. Aluminum samples were also analyzed as a reference material to validate both the experimental methodology and the MCNP6 simulations. The obtained attenuation coefficients were compared with NIST XCOM reference data, showing maximum deviations of 4.94% for the [137]Cs measurements (aluminum) and 9.50% for the corrected [241]Am measurements (PLA). Results demonstrated that the proposed correction methodology significantly improved agreement between experimental and reference attenuation coefficients, particularly for low-energy photons strongly affected by Rayleigh scattering and geometrical effects. The proposed approach enables reliable attenuation measurements to be performed even under non-ideal experimental conditions, particularly in laboratories without access to highly collimated measurement systems or high-intensity radioactive sources. This methodology contributes to the assessment of 3D-printed polymers as alternative materials for radiation shielding and nuclear technology applications.

Accurate quantification of condensate film thickness in large-diameter wet gas transmission pipelines represents one of the more challenging problems in nuclear measurement technology. In misty flow, liquid is distributed as fine aerosol droplets across the full cross-section of the pipe, making conventional gamma densitometry unreliable. A preconditioner system - consisting of a static helical impeller and a circular nozzle - converts this misty regime into a quasi-annular configuration in which the liquid coalesces as a continuous film on the inner pipe wall, making the geometry tractable for circumferential gamma scanning. This paper presents a Geant4/GATE Monte Carlo radiation transport study of a 360-detector circumferential scanning array designed for a full-scale 32-inch (DN-800, Schedule 40) steel pipe with an outer diameter of 813 mm, inner diameter of 778.04 mm, and wall thickness of 17.48 mm. A [137]Cs source of 40 mCi activity, positioned at the geometric centre of the pipe, was modelled with an axial lead collimator reproducing the laboratory pencil beam. Three condensate-equivalent calibration phantom sectors - 15 mm, 20 mm, and 27 mm thick, wood, density 750 kg/m[3], angular opening 67° - were placed against the inner wall at angular separations of 20°, 51°, and 88°. The model was implemented in two campaigns: the original thesis configuration, and a fully-documented reconstruction (Geant4 11.0.0/GATE 9.2) incorporating explicit lead collimation, a 7% detector energy-resolution model, and 1.92×10[9] primary histories. The reconstructed run gives sector relative count rates of 0.9255 ± 0.0011, 0.9021 ± 0.0011 and 0.8696 ± 0.0011 for the 15, 20 and 27 mm phantoms, referenced to the condensate-free gas region at a no-film baseline of 13144 cps per detector element. Weighted regression yields an effective linear attenuation coefficient of μ=0.00517±0.00003mm[-1] for the wood phantom at 0.750 g/cm[3], and inverting this calibration recovers the three film thicknesses to within 0.06 mm of nominal. Angular detection errors across six measurement zones range from 0.42% to 2.06%, with a total circuit error of only 0.04% for the complete 360° rotation. Agreement between the legacy and reconstructed campaigns is 2.1% in normalised RMSE across the sampled regions. The central-source configuration yields a baseline count rate approximately seven times higher than an equivalent external-source arrangement, owing to the halving of steel wall traversal. To the authors' knowledge, this is the first reported Monte Carlo optimisation study of a circumferential gamma scanning array at DN-800 scale, providing a validated computational framework for annular film profiling in large-bore wet gas infrastructure.