Search Results (114)

Intelligent Reflective Surface (IRS)-aided Multiple-Input Multiple-Output (MIMO) systems have emerged as a promising solution to enhance spectral and energy efficiency in future wireless communications. However, accurate channel estimation remains a key challenge due to the passive nature and high dimensionality of IRS channels. This paper proposes a lightweight hybrid framework for cascaded channel estimation by combining a physics-based Bilinear Alternating Least Squares (BALS) algorithm with a deep neural network named ConvTrans-ResNet. The network integrates convolutional embeddings and Transformer modules within a residual learning architecture to exploit both local and global spatial features effectively while ensuring training stability. A series of ablation studies is conducted to optimize architectural components, resulting in a compact configuration with low parameter count and computational complexity. Extensive simulations demonstrate that the proposed method significantly outperforms state-of-the-art neural models such as HA02, ReEsNet, and InterpResNet across a wide range of SNR levels and IRS element sizes in terms of the Normalized Mean Squared Error (NMSE). Compared to existing solutions, our method achieves better estimation accuracy with improved efficiency, making it suitable for practical deployment in IRS-aided systems. Full article

This study presents a comparative analysis of wave buoy analogy models for sea state estimation. A nonparametric, response amplitude operator-based model is introduced as a physics-based approach, while a convolutional neural network is adopted as a machine learning approach. Using time-domain simulation data of wave-induced ship motions under various operating conditions, the accuracy and reliability of each model’s estimation are evaluated. The sensitivity of the physics-based model to operating conditions is examined, along with optimization strategies such as hyperparameter tuning. In particular, regularization techniques based on bilinear and B-spline surface fitting are applied to the nonparametric model, and the effects of interpolation techniques on model performance are assessed. For the machine learning model, a parametric study is conducted to determine input data types and formats, including time series and spectral representations, as well as the required length of the time window and dataset volume. Finally, the feasibility of the proposed neural network in estimating not only sea state parameters but also loading and navigational information, such as ship speed and GM, is discussed. Full article

Background: Published studies have documented the association between sunshine duration and depression symptoms; however, the evidence regarding the long-term effects and potential mechanisms remains insufficient. This study aimed to examine the association between sunshine duration and incident depression and to explore potential mediating pathways. Methods: A total of 336,805 participants from the UK Biobank were included in the study. Meteorological exposures were estimated using the bilinear interpolation approach and time-weighted method. The association between sunshine duration and incident depression was examined through the time-dependent Cox proportional hazard model and generalized propensity score model. Vitamin D, calcium, immune biomarkers, an aggregated inflammation score (INFLA-score), and sleep pattern were selected as the potential mediators. Causal mediation analysis was employed to elucidate underlying mediating effects. Results: With a median follow-up of 13 years, 13,862 cases of incident depression were identified. Sunshine duration demonstrated a negative association with the incident depression. The effects were stronger among the elderly, alcohol consumers, individuals who spent less time outdoors, and those who were less physically active. Vitamin D, calcium, INFLA, neutrophils, and monocytes emerged as the top five contributors of immune biomarkers to the natural indirect effect. The combined mediating effect of top five biomarkers and sleep pattern accounted for 30% of the total effect of sunshine duration on the incident depression. Conclusion: Our study suggests that longer sunshine duration might mitigate depression through vitamin D-related metabolism, inflammation, and sleep pattern. It may serve as an effective natural antidepressant, particularly for the elderly, alcohol consumers, less outdoor spenders, and those who were less physically active. Full article

The confined space of coal mines characterized by curved tunnels with rough surfaces and a variety of deployed production equipment induces severe signal attenuation and interruption, which significantly degrades the accuracy of conventional channel estimation algorithms applied in coal mine wireless communication systems. To address these challenges, we propose a modified Bilinear Generalized Approximate Message Passing (mBiGAMP) algorithm enhanced by intelligent reflecting surface (IRS) technology to improve channel estimation accuracy in coal mine scenarios. Due to the presence of abundant coal-carrying belt conveyors, we establish a hybrid channel model integrating both fast-varying and quasi-static components to accurately model the unique propagation environment in coal mines. Specifically, the fast-varying channel captures the varying signal paths affected by moving conveyors, while the quasi-static channel represents stable direct links. Since this hybrid structure necessitates an augmented factor graph, we introduce two additional factor nodes and variable nodes to characterize the distinct message-passing behaviors and then rigorously derive the mBiGAMP algorithm. Simulation results demonstrate that the proposed mBiGAMP algorithm achieves superior channel estimation accuracy in dynamic conveyor-affected coal mine scenarios compared with other state-of-the-art methods, showing significant improvements in both separated and cascaded channel estimation. Specifically, when the NMSE is ${10}^{-3}$, the SNR of mBiGAMP is improved by approximately 5 dB, 6 dB, and 14 dB compared with the Dual-Structure Orthogonal Matching Pursuit (DS-OMP), Parallel Factor (PARAFAC), and Least Squares (LS) algorithms, respectively. We also verify the convergence behavior of the proposed mBiGAMP algorithm across the operational signal-to-noise ratios range. Furthermore, we investigate the impact of the number of pilots on the channel estimation performance, which reveals that the proposed mBiGAMP algorithm consumes fewer number of pilots to accurately recover channel state information than other methods while preserving estimation fidelity. Full article

This paper presents a bilinear framework for real-time speech source separation and dereverberation tailored to wearable augmented reality devices operating in dynamic acoustic environments. Using the Speech Enhancement for Augmented Reality (SPEAR) Challenge dataset, we perform extensive validation with real-world recordings and review key algorithmic parameters, including the forgetting factor and regularization. To enhance robustness against direction-of-arrival (DOA) estimation errors caused by head movements and localization uncertainty, we propose a region-of-interest (ROI) beamformer that replaces conventional point-source steering. Additionally, we introduce a multi-constraint beamforming design capable of simultaneously preserving multiple sources or suppressing known undesired sources. Experimental results demonstrate that ROI-based steering significantly improves robustness to localization errors while maintaining effective noise and reverberation suppression. However, this comes at the cost of increased high-frequency leakage from both desired and undesired sources. The multi-constraint formulation further enhances source separation with a modest trade-off in noise reduction. The proposed integration of ROI and LCMP within the low-complexity frameworks, validated comprehensively on the SPEAR dataset, offers a practical and efficient solution for real-time audio enhancement in wearable augmented reality systems. Full article

This study introduces a novel energy-based inverse method for estimating residual stresses in structures composed of one-dimensional elements undergoing elastic–plastic deformation. The problem is reformulated as a global optimization task governed by the principle of minimum total potential energy. Rather than solving equilibrium equations directly, the internal stress distribution is inferred by minimizing the structure’s total potential energy using a real-coded genetic algorithm. This approach avoids gradient-based solvers, matrix assembly, and incremental loading, making it suitable for nonlinear and history-dependent systems. Plastic deformation is encoded through element-wise stress-free lengths, and a dynamic fitness exponent strategy adaptively controls selection pressure during the evolutionary process. The method is validated on single- and multi-bar truss structures under axial tensile loading, using a bilinear elastoplastic material model. The results are benchmarked against nonlinear finite element simulations and analytical calculations, demonstrating excellent predictive capability with stress errors typically below 1%. In multi-material systems, the technique accurately reconstructs tensile and compressive residual stresses arising from elastic–plastic mismatch using only post-load geometry. These results demonstrate the method’s robustness and accuracy, offering a fully non-incremental, variational alternative to traditional inverse approaches. Its flexibility and computational efficiency make it a promising tool for residual stress estimation in complex structural applications involving plasticity and material heterogeneity. Full article

This paper investigates the joint state and parameter estimation issue of the bilinear state–space system with non-Gaussian process noise and non-Gaussian measurement noise. Tackling such an issue is challenging because either of these noises may seriously degrade the estimation performance. To significantly counteract the negative effect of these non-Gaussian noises, a Gaussian–Versoria mixed kernel correntropy (GVMKC)-based cost function is introduced by integrating two different types of kernel functions into a mixed kernel. Subsequently, a GVMKC-based Kalman filtering and a GVMKC-based robust recursive least squares method are derived for estimating the system states and parameters, respectively. Thus, a robust joint parameter and state estimation method is developed by implementing the interactive computation. The effectiveness of the proposed method is confirmed by simulation examples. Full article

This study investigates the long-term flexural performance of prestressed concrete double tees under sustained loading. Six full-scale specimens were subjected to a comprehensive experimental program, including a 320-day storage period following prestress release, a short-term flexural test, and a 990-day sustained loading phase. Mid-span deflections were measured using a string-line method, while the effective prestress in tendons was continuously monitored with fiber Bragg grating (FBG) sensors. Results showed a pronounced increase in camber during the storage phase, with long-term camber reaching approximately three times the initial value. Under short-term loading, the slabs exhibited a clear bilinear moment–deflection behavior. During sustained loading, most of the long-term deflection developed in the early stages, and an inverse relationship between load level and deflection growth was observed. Additionally, data from 20 short-term tests were compiled, and a bilinear stiffness model was proposed to estimate flexural stiffness in the cracked state. These findings contribute to a deeper understanding of long-term deformation in prestressed concrete double tees and provide reference data for serviceability evaluation and design refinement. Full article

In the present work, we give a new proof of the well-known Selberg’s inequality in complex Hilbert spaces from an operator-theoretic perspective, establishing its fundamental equivalence with the Cauchy–Bunyakovsky–Schwarz inequality. We also derive several lower and upper bounds for the Selberg operator, including its norm estimates, refining classical results such as de Bruijn’s and Bohr’s inequalities. Additionally, we revisit a recent claim in the literature, providing a clarification of the conditions under which Selberg’s inequality extends to abstract bilinear forms. Full article

The patch loading resistance of slender steel plate girders is a critical factor in the design of launched steel and composite steel–concrete bridges. Traditional design methods enhance patch loading resistance through various stiffening techniques, with contributions typically estimated via code expressions calibrated on experimental data that do not always reflect the complexities of full-scale bridge applications. Finite Element (FE) modeling offers a more realistic alternative, though its practical application is often hindered by modeling uncertainties and nonlinearities. To bridge this gap, this paper introduces an advanced FE modeling approach. It provides a comprehensive description of an FE model that accurately predicts both the load–displacement behavior and the patch loading resistance. The model is benchmarked against a broad set of experimental tests and systematically investigates the effects of key modeling parameters and their interactions—material stress–strain law, boundary condition representation, stiffness of the load introduction area, initial geometric imperfections, and solving algorithms. Key findings demonstrate that a bilinear elastoplastic material model with hardening is sufficient for estimating ultimate resistance, and kinematic constraints can effectively replace rigid transverse stiffeners. The stiffness of the load application zone significantly influences the response, especially in launched bridge scenarios. Initial imperfections notably affect both stiffness and strength, with standard fabrication tolerances offering suitable input values. The modified Riks algorithm is recommended for its efficiency and stability in nonlinear regimens. The proposed methodology advances the state of practice by providing a simple yet reliable FE modeling approach for predicting patch loading resistance in real-world bridge applications, leading to safer and more reliable structural designs. Full article

We will show, in any space dimension $d\ge 3$, the decay and scattering in the energy space for the solution to the damped nonlinear Schrödinger equation posed on ${\mathbb{R}}^d\times \mathbb{T}$ and initial data in $H^1({\mathbb{R}}^d\times \mathbb{T})$. We will also derive new bilinear Morawetz identities and corresponding localized Morawetz estimates. Full article

Remotely sensed data, including rainfall estimates and digital elevation models (DEMs), are increasingly available at various temporal and spatial scales, offering new opportunities for rainfall regionalization in regions with limited ground-based observations. We evaluate the efficacy of NASA’s Integrated Multi-satellitE Retrievals for GPM (IMERG) rainfall estimates and SRTM-derived elevation data as alternative spatial covariates for regionalizing average and extreme rainfall patterns across Greece. Using the Bilinear Surface Smoothing (BSS) framework, we assess and compare the regionalization of average daily rainfall and average annual maximum rainfall across multiple timescales (0.5 h to 48 h) by leveraging both IMERG-derived estimates and the elevation data as covariates. Additionally, the BSS framework is herein extended to provide Bayesian credible intervals for the final estimates, using the posterior variance estimate and the equivalent degrees of freedom determined through the Generalized Cross Validation error minimization procedure. Elevation-based models outperformed IMERG, particularly for indices of extreme rainfall, capturing the differential effects of orography. The exploration of the orographic effect based on the BSS framework revealed that the average annual rainfall maxima at small timescales exhibit a negative relation to elevation, which becomes positive and more significant with increasing timescale. However, IMERG proved valuable for regionalizing average daily rainfall, demonstrating its utility as a complementary tool. The results also underscore the role of temporal scale in regionalization efficiency of extreme rainfall, with higher accuracy observed at longer timescales (24 h and 48 h) and greater uncertainty at finer scales. Full article

This paper presents the fatigue limit of flax/PLA composites with different fiber reinforcement architectures. The configurations of the analyzed flax/PLA composites are [0°]₈, [0°/90°]_s, [+45°/−45°]_s, [90°]₄, stacking sequences, and basket weave laminates. The methods used to estimate the fatigue limit are the fitting of stress versus number of cycles data using Weibull and Basquin equations, the surface thermographic technique with bilinear and exponential models to analyze the evolution of temperature increment, and volumetric dissipated energy. According to the results found, superficial temperature and the maximum strain reached stabilization over 2000 cycles for σ_max/σ_ut < 0.7, which was used to determine cyclic stress–strain curves and the fatigue limit. The cyclic stress–strain shows a nonlinear behavior for all laminates, having a good correlation to the Ramberg–Osgood model. Furthermore, having the stabilized temperature and volumetric dissipated energy, the exponential model was used to evaluate the fatigue limit and compared to the values found by Basquin and bilinear models. The fatigue limit found by Basquin and bilinear models shows conservative values compared to the exponential models. The results also show that temperature measurement using infrared thermography is quite sensitive to the environmental temperature variation, especially at low stress applied, and finally, the comparison of these methods on different reinforcement configurations provides a guide to select a proper technique in each case. Full article

In this paper, we investigated a three-dimensional incompressible fractional rotating magnetohydrodynamic (FrMHD) system by reformulating the Cauchy problem into its equivalent mild formulation and working in critical homogeneous Sobolev spaces. For this, we first established the existence and uniqueness of a global mild solution for small and divergence-free initial data. Moreover, our approach is based on proving sharp bilinear convolution estimates in critical Sobolev norms, which in turn guarantee the uniform analyticity of both the velocity and magnetic fields with respect to time. Furthermore, leveraging the decay properties of the associated fractional heat semigroup and a bootstrap argument, we derived algebraic decay rates and established the long-time dissipative behavior of FrMHD solutions. These results extended the existing literature on fractional Navier–Stokes equations by fully incorporating magnetic coupling and Coriolis effects within a unified fractional-dissipation framework. Full article

The white-noise-driven KdV-type Boussinesq system is a class of stochastic partial differential equations (SPDEs) that describe nonlinear wave propagation under the influence of random noise—specifically white noise—and generalize features from both the Korteweg–de Vries (KdV) and Boussinesq equations. We consider a Cauchy problem for two stochastic systems based on the KdV-type Boussinesq equations. For these systems, we determine sufficient conditions to ensure that this problem is locally and globally well posed for initial data in Sobolev spaces by the linear and bilinear estimates and their modification together with the Banach fixed point. Full article