Search Results (1,087)

In a cellular maritime communication system, ocean buoys are essential to enable environmental monitoring, offshore platform management, and disaster response. Therefore, energy-efficient transmission from the buoys is a key requirement to prolong their operational time. A fixed uplink beamforming can be considered to save energy by leveraging its beam gain while managing the target link reliability. However, the dynamic condition of ocean waves causes buoys’ random orientation, leading to frequent misalignment of their predefined beam direction aimed at the base station, which degrades both the link reliability and energy efficiency. To address this challenge, we propose a wave-adaptive beamforming framework to satisfy data-rate demands within limited power budgets. This strategy targets scenarios where sea state information is unavailable, such as in network-assisted systems. We propose a Two-Dimensional Thompson Sampling (2DTS) scheme that jointly selects beamwidth and transmit power to satisfy the target-rate constraint with minimal power consumption and thus achieve maximal energy efficiency. This adaptive learning approach effectively balances exploration and exploitation, enabling efficient operation in uncertain and changing sea conditions. In simulation, under a moderate sea state, 2DTS achieves an energy efficiency of $1.26 \times {10}^4$ bps/Hz/J at round 600, which is $73.7\%$ of the ideal ($1.71 \times {10}^4$), and yield gains of $96.9\%$ and $447.8\%$ over exploration-based TS and conventional TS, respectively. Under a harsh sea state, 2DTS attains $3.09 \times {10}^4$ bps/Hz/J ($85.6\%$ of the ideal $3.61 \times {10}^4$), outperforming the exploration-based and conventional TS by $83.9\%$ and $113.1\%$, respectively. The simulation results demonstrate that the strategy enhances energy efficiency, confirming its practicality for maritime communication systems constrained by limited power budgets. Full article

Background: Gestational diabetes mellitus (GDM) is associated with subclinical alterations in fetal cardiac morphology and function. Ursodeoxycholic acid (UDCA), widely used in pregnancy for intrahepatic cholestasis, has demonstrated cardioprotective properties in experimental fetal models, preventing conduction abnormalities and improving myocardial function. Whether UDCA modifies fetal or neonatal cardiac adaptation in GDM pregnancies has not been previously investigated. The objective was to evaluate the effect of ursodeoxycholic acid (UDCA) on fetal and neonatal cardiac function in pregnancies complicated by gestational diabetes mellitus (GDM). Methods: In this randomized, placebo-controlled study, 113 women with GDM were enrolled, of whom 56 received UDCA and 57 the placebo. After measurement of maternal blood UDCA concentrations, 43 participants in the treatment group had levels ≥0.5 µmol/L and were included in the per-protocol analysis. Echocardiographic and Doppler-derived cardiac indices were assessed at baseline, 36 weeks’ gestation, and postpartum. Comparisons were performed using univariable tests and mixed-effects multivariable models accounting for time and treatment. Results: In the treatment group, compared to the placebo group, there were no significant differences in cardiac indices at 36 weeks’ gestation or postpartum when assessed individually. However, in the mixed-effects longitudinal analysis, a significant treatment-by-time interaction was observed. Specifically, in the postpartum period, mitral A-wave velocity (MV-A) was higher in the treatment group compared to that under the placebo (9.58, 95% CI 2.29–16.87; p = 0.010), reflecting a more pronounced increase in the atrial contribution to left ventricular filling over time. Similarly, aortic peak velocity (Ao_Vmáx) was significantly higher in the treatment group compared to that under the placebo in the postpartum period (7.97, 95% CI 0.19–15.75; p = 0.045), indicating a greater augmentation in left ventricular outflow dynamics. Conclusions: In pregnancies complicated by GDM, UDCA did not lead to significant cross-sectional differences in fetal or neonatal cardiac indices at 36 weeks or postpartum. However, longitudinal modeling indicated that UDCA was associated with a greater increase in the atrial contribution to ventricular filling (MV-A) and aortic peak velocity (Ao_Vmáx) in the postpartum period compared to that under the placebo. These findings suggest that while UDCA does not broadly alter cardiac function, it may modulate specific aspects of diastolic filling and systolic outflow dynamics during late gestation and early neonatal adaptation. Full article

This paper addresses the critical multiple-uncertainty challenge in laser link acquisition for space gravitational wave detection missions—a key bottleneck where spacecraft attitude alignment for laser link establishment is perturbed by inherent random disturbances in such missions, while also needing to balance ultra-high attitude precision, fuel efficiency, and compliance with engineering constraints. To tackle this, a convex optimization-based attitude control strategy integrating covariance control and free terminal time optimization is proposed. Specifically, a stochastic attitude dynamics model is first established to explicitly incorporate the aforementioned random disturbances. Subsequently, an objective function is designed to simultaneously minimize terminal state error and fuel consumption, with three key constraints (covariance constraints, pointing constraints, and torque saturation constraints) integrated into the convex optimization framework. Furthermore, to resolve non-convex terms in chance constraints, this study employs a hierarchical convexification method that combines Schur’s complementary theorem, second-order cone relaxation, and Taylor expansion techniques. This approach ensures lossless relaxation, renders the optimization problem computationally tractable without sacrificing solution accuracy, and overcomes the shortcomings of traditional convexification methods in handling chance constraints. Finally, numerical simulations demonstrate that the proposed method adheres to engineering constraints while maintaining spacecraft attitude errors below 1 μrad under environmental uncertainties. This study provides a convex optimization solution for laser link acquisition in space gravitational wave detection missions considering uncertainty conditions, and its framework can be extended to the optimal design of other stochastically uncertain systems. Full article

Automatic mode-locking is a crucial approach for achieving ultrashort pulses in fiber lasers. Here, a random search algorithm was developed, and an automatic mode-locked laser was constructed. Numerical simulations of an automatic mode-locked Yb-doped fiber laser were conducted, and both continuous-wave, as well as mode-locked pulse states, were successfully obtained. The laser utilized a squeezer-type electrically controlled polarization controller to adjust the mode-locking states and enabled the controllable output of 532.71 fs dissipative solitons and 23.87 ps noise-like pulses, with search times of 14.19 s and 2.37 s, respectively. The center wavelengths were 1034 nm and 1038 nm, with signal-to-noise ratios of 63.1 dBm and 51.2 dBm, respectively. This work effectively addresses the polarization state drift caused by temperature and vibration, enhancing the laser’s environmental adaptability through adaptive monitoring. Full article

Due to its sensitivity to topographic and land use land cover features, air temperature (maximum, minimum, and mean—T_x, T_n, and T_mean) is extremely variable in space and time. The sparse and unevenly distributed meteorological stations observed across remote regions cannot monitor such variability. Freely available, gridded temperature datasets (T-datasets) are positioned as an opportunity to overcome this issue. Still, their coarse spatial resolution (i.e., ≥5 km) does not allow for the observation of air temperature variations on a fine spatial scale. In this context, a set of variables that have a close relationship with daily air temperature (MODIS maximum, minimum, and mean Land Surface Temperature—LST_x, LST_n, and LST_mean; MODIS NDVI; SRTM topographic features—elevation, slope, and aspect) are integrated in three regression machine-learning models (Random Forest—RF, eXtreme Gradient Boosting—XGB, Multiple Linear Regression—MLR) to propose a T-dataset estimates (T_x, T_n, and T_mean) spatial resolution downscaling framework. The approach consists of two main steps: firstly, the machine-learning models are trained at the native 5 km spatial resolution of the studied T-dataset (i.e., CHIRTS); secondly, the application of the trained machine-learning models at a 1 km spatial resolution to downscale CHIRTS from 5 km to 1 km. The results show that the method not only improves the spatial resolution of the CHIRTS dataset, but also its accuracy, with higher improvements for T_n than for T_x and T_mean. Among the considered models, RF performs the best, with an R², RMSE, and MAE improvement of 2.6% (0%), 47.1% (6.1%), and 55.3% (7%) for T_n (T_x). These results will support air temperature monitoring and related extreme events such as heat and cold waves, which are of prime importance in the actual climate change context. Full article

As the influence of indoor environments on human emotional regulation and cognitive function becomes increasingly critical in modern society, there is a growing need for intelligent lighting systems that dynamically respond to users’ emotional states. While previous studies have investigated either illuminance or color in isolation, this study concentrates on quantitatively analyzing the interaction of these two key elements on human emotion and cognitive control capabilities. Utilizing electroencephalography (EEG) and electrocardiography (ECG) signals, we measured participants’ physiological responses and subjective emotional assessments in 18 unique lighting conditions, combining six colors and three levels of illuminance. The results confirmed that the interaction between light color and illuminance significantly affects physiological indicators related to emotion regulation. Notably, low-illuminance purple lighting was found to promote positive emotions and inhibit negative ones by increasing frontal alpha asymmetry (FAA) and gamma wave activity. Conversely, low-illuminance environments generally diminished cognitive reappraisal and negative emotion inhibition capabilities. Furthermore, a random forest model integrating time-series data from EEG and ECG predicted emotional valence and arousal with accuracies of 87% and 79%, respectively, demonstrating the validity of multi-modal physiological signal-based emotion prediction. This study provides empirical data and a theoretical foundation for the development of human-centered, emotion-adaptive lighting systems by presenting a quantitative causal model linking lighting, physiological responses, and emotion. These findings also provide a biomimetic perspective by linking lighting-induced physiological responses with emotion regulation, offering a foundation for the development of adaptive lighting systems that emulate natural light–human interactions. Full article

This study presents a machine learning-based framework for classifying five embroidered stitch patterns—straight, zigzag, joining, satin, and wave—under 10% tensile strain, aiming to enhance their utility in smart textile circuits. Electrical conductivity was derived from resistance data and standardized using Z-score normalization. Conductivity sequences were first analyzed with PCA and Random Forest classifiers, then classified using a structural artificial neural network model. The model employed a structurally informed filter design, reflecting stitch-wise signal periodicity to capture time-varying electrical patterns under cyclic strain. It achieved a test accuracy of 97.33%, with F1-scores above 0.83 for all classes and perfect scores in three. Partial confusion between wave and zigzag patterns was observed due to their similar curved geometry and signal profiles. These results validate the discriminative power of conductivity-based features and demonstrate the potential of structure-aware neural networks for identifying dynamic stitched circuits in smart textiles. Full article

Post-disaster underground (UG) mine environments are characterized by complex and rapidly changing conditions, adding extra attenuation to propagating electromagnetic (EM) waves. One such complex condition is the extreme generation of dust and sudden rise in humidity contributing to extra attenuation effects to propagating waves, especially under varying airborne humidity and dust levels. The existing wave propagation prediction models, especially those that factor in the effect of dust particles, are deterministic in nature, limiting their ability to account for uncertainties, especially during emergency conditions. In this work, the vector parabolic equation (VPE) model is modified to include dust attenuation effects. Using the complex permittivity of dust as a random variable, the Karhunen–Loève (KL) expansion is used to generate random samples of permittivity along the drifts for which each realization is solved using deterministic VPE method. The model is validated using a modified Friis method and experimentally obtained data from literature. The findings show that accounting for dust and humidity effects stochastically captures the extra losses that would have otherwise been lost using deterministic methods. The proposed framework offers key insights for designing resilient underground wireless systems, strengthening miner tracking, and improving safety during emergencies. Full article

In this work, we numerically investigate the fractional clannish random walker’s parabolic equations (FCRWPEs) and the nonlinear fractional Cahn–Allen (NFCA) equation using the Hybrid Decomposition Method (HDM). The analysis uses the Atangana–Baleanu fractional derivative (ABFD) in the Caputo sense, which has a nonsingular and nonlocal Mittag–Leffler kernel (MLk) and provides a more accurate depiction of memory and heredity effects, to examine the dynamic behavior of the models. Using nonlinear analysis, the uniqueness of the suggested models is investigated, and distinct wave profiles are created for various fractional orders. The accuracy and effectiveness of the suggested approach are validated by a number of example cases, which also support the approximate solutions of the nonlinear FCRWPEs. This work provides significant insights into the modeling of anomalous diffusion and complex dynamic processes in fields such as phase transitions, biological transport, and population dynamics. The inclusion of the ABFD enhances the model’s ability to capture nonlocal effects and long-range temporal correlations, making it a powerful tool for simulating real-world systems where classical derivatives may be inadequate. Full article

The mechanisms of freak wave generation in 3D island-reef topography are investigated. Four types of freak waves are investigated, based on the wavelet transform for examining the characteristics of freak waves and their mechanism. The freak waves come from a three-dimensional experimental terrain model in a random wave. The wavelet energy spectrum, scale-averaged and time-averaged wavelet spectrum are considered. A new parameter (scale-centroid wavelet spectrum) is defined, based on the wavelet transform algorithm, to quantitatively analyze and further estimate the energy transfer process. The results suggest that the occurrence of freak waves is associated with the gradual alignment of the phases of wave components. The nonlinear interaction in terms of wavelet cross-bispectrum implies that wave–wave interaction, especially with high-frequency components, is obviously enhanced during a freak wave occurrence. The energy transforms to a high frequency during a freak wave occurrence. The current result forms a definite indication that the occurrence of freak waves is caused by the combined effects of linear superposition and nonlinear interactions. Linear superposition begins to take effect long before the freak wave occurs, whereas nonlinear interactions primarily occur during the shorter period just before the freak wave forms. It provides an important reference for the prediction of abnormal waves. Full article

Integrated sensing and communication (ISAC) has emerged as a transformative technology for intelligent transportation systems. Index modulation (IM), recognized for its high robustness and energy efficiency (EE), has been successfully incorporated into ISAC systems. However, most existing IM-based ISAC schemes overlook the spatial multiplexing potential of millimeter-wave channels and remain confined to single-hop vehicle-to-vehicle (V2V) setups, failing to address the challenges of energy consumption and noise accumulation in real-world multi-hop V2V networks with complex road topologies. To bridge this gap, we propose a spatial scattering modulation-based ISAC (ISAC-SSM) scheme and introduce it to multi-hop V2V networks. The proposed scheme leverages the sensed positioning information to select maximum signal-to-noise ratio relay vehicles and employs a detect-amplify-and-forward (DAF) protocol to mitigate noise propagation, while utilizing sensed angle data for Doppler compensation to enhance communication reliability. At each hop, the transmitter modulates index bits on the angular-domain spatial directions of scattering clusters, achieving higher EE. We initially derive a closed-form bit error rate expression and Chernoff upper bound for the proposed DAF ISAC-SSM under multi-hop V2V networks. Both theoretical analyses and Monte Carlo simulations have been made and demonstrate the superiority of DAF ISAC-SSM over existing alternatives in terms of EE and error performance. Specifically, in a two-hop network with 12 scattering clusters, compared with DAF ISAC-conventional spatial multiplexing, DAF ISAC-maximum beamforming, and DAF ISAC-random beamforming, the proposed DAF ISAC-SSM scheme can achieve a coding gain of 1.5 dB, 2 dB, and 4 dB, respectively. Moreover, it shows robust performance with less than a 1.5 dB error degradation under 0.018 Doppler shifts, thereby verifying its superiority in practical vehicular environments. Full article

The Ground Penetrating Radar (GPR) method can image dielectric discontinuities in subsurface structures, which cause the reflection of electromagnetic (EM) waves. These discontinuities are imaged as reflectors in GPR sections, often distorted by diffracted energy. To focus the diffracted energy within the GPR sections, migration is commonly used. The migration velocity of GPR data is a low-wavenumber attribute crucial for effective migration. Obtaining a migration velocity model, typically close to a Root Mean Square (RMS) model, from zero-offset (ZO) data requires analysis of the available diffractions, whose density and (x, t) coverage are random. Thus, the accuracy and efficiency of such a velocity model, whether for migration or interval velocity model estimation, are not guaranteed. An alternative is the multipath summation method, which involves the weighted stacking of constant velocity migrated sections. Each stacked section contributes to the final stack, weighted by a scalar value dependent on the constant velocity value used and its relation to its estimated mean velocity of the section. This method effectively focuses the GPR diffractions in the presence of low heterogeneity. However, when the EM velocity varies dramatically, 2D weights are needed. In this study, with the aid of an Artificial Intelligence (AI) algorithm that detects diffractions and uses their kinematic information, we generate a diffraction velocity model. This model is then used to assign 2D weights for the weighted multipath summation, aiming to focus the scattered energy within the GPR section. We describe this methodology and demonstrate its application in enhancing the lateral continuity of reflections. We compare it with the 1D multipath summation using simulated data and present its application on marble assessment GPR data for imaging cracks and discontinuities in the subsurface structure. Full article

Evaporation Duct Height (EDH) is a crucial parameter in evaporation duct modeling, as it directly influences the strength of the waveguide trapping effect and significantly impacts the over-the-horizon detection performance of maritime radars. To address the limitations of low prediction accuracy and limited interpretability in existing deep learning models under complex marine meteorological conditions, this study proposes a surrogate model, BLA-EDH, designed to emulate the output of the Naval Postgraduate School (NPS) model for real-time EDH estimation. Experimental results demonstrate that BLA-EDH can effectively replace the traditional NPS model for real-time EDH prediction, achieving higher accuracy than Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) models. Random Forest analysis identifies relative humidity (0.2966), wind speed (0.2786), and 2-m air temperature (0.2409) as the most influential environmental variables, with importance scores exceeding those of other factors. Validation using the parabolic equation shows that BLA-EDH attains excellent fitting performance, with coefficients of determination reaching 0.9999 and 0.9997 in the vertical and horizontal dimensions, respectively. This research provides a robust foundation for modeling radio wave propagation in the Yellow Sea and Bohai Sea regions and offers valuable insights for the development of marine communication and radar detection systems. Full article

This paper presents an intelligent design framework for a high-performance 26 GHz MIMO antenna array tailored to 5G applications, built upon a compact single-element patch. The 11.5 mm × 11.5 mm × 1.6 mm microstrip patch on FR4 exhibits near-unity electrical length, an ultra-deep return loss (S11 < −40 dB at 26 GHz), and a wide operational bandwidth from 24.4 to 31.2 GHz (6.8 GHz, ~26.2%). A two-element array, spaced at λ/2, is first augmented with a inspired metamaterial (MTM) unit cell whose dimensions are optimized via a Multi-Layer Perceptron (MLP) model to maximize gain (+2 dB) while preserving S11. In the second phase, a closed-square parasitic ring is introduced between the elements; its side length, thickness, and position are predicted by a Random Forest (RF) model with Bayesian optimization to minimize mutual coupling (S12) from −25 dB to −58 dB at 26 GHz without significantly degrading S11 (remains below −25 dB). Full-wave simulations and anechoic chamber measurements confirm the ML predictions. The close agreement among predicted, simulated, and measured S-parameters validates the efficacy of the proposed AI-assisted optimization methodology, offering a rapid and reliable route to next-generation millimeter-wave MIMO antenna systems. Full article

Background: Extracorporeal Shockwave Therapy (ESWT) for targeting myofascial tissues is gaining increasing interest for the treatment of musculoskeletal disorders. This review evaluates the mechanisms, applications, and effectiveness of ESWT in managing myofascial pain syndrome (MPS) while identifying methodological gaps in existing research. Methods: A systematic search of PubMed, PEDro, and Cochrane Central Library was conducted up to August 2025, focusing on studies from existing meta-analyses, particularly randomized controlled trials. Eligible studies were selected based on predefined criteria, including the use of ESWT for MPS treatment, methodological rigor, and adherence to standardized protocols. Data were extracted on diagnostic criteria for MPS and myofascial trigger points (MTrPs), shockwave application parameters, adherence to International Society for Medical Shockwave Treatment (ISMST) guidelines, follow-up periods, and treatment efficacy. Results: significant inconsistencies were identified in MPS diagnosis, shockwave application technique, and study follow-up periods. Many studies did not adhere to ISMST guidelines, with variations in energy levels, impulses, and differentiation between radial pressure wave (RPW) and focused ESWT (fESWT). One-third of the studies had follow-up periods of two weeks or less, limiting the assessment of long-term outcomes. Despite these limitations, ESWT demonstrated moderate to good efficacy compared with controls. Conclusions: While ESWT appears effective for MPS, methodological inconsistencies prevent definitive conclusions. Future research should standardize protocols, differentiate RPW from fESWT, and include longer follow-up periods to optimize therapeutic potential and validate ESWT as a treatment for MPS. Full article