Search Results (266)

A digital predistortion (DPD) scheme for concurrent multi-beam transmission in fully digital multiple-input, multiple-output (MIMO) systems, using Fast Fourier Transform (FFT) beamforming and so-called virtual-array processing, is proposed. In a MIMO array with nonlinear power amplifiers (PAs), transmitting multiple beams concurrently yields intermodulation products that end up in both user and non-user directions. In the setting with few users in a large array, the array dimension will typically be much larger than the number of generated intermodulation products. At the same time, linearization per PA is excessively costly for large arrays. This work shows that it is instead possible to linearize the system by producing predistorted user beams, and non-user intermodulation products, through DPD processing in a virtual array of a much smaller dimension than the physical array. Theoretical derivations and simulation examples show how this approach can lead to manyfold reductions in DPD complexity. Full article

Background: Women face underdiagnosed cardiovascular disease (CVD)/stroke risks due to sex-specific pathophysiological mechanisms, including hormonal variations such as oestrogen decline, adverse pregnancy outcomes (APOs), endothelial dysfunction, autoimmune-mediated factors, and sexual dimorphism in cardiac remodelling. Conventional risk assessment tools, predominantly calibrated to male pathophysiology, lack sensitivity in detecting these female-specific determinants. We hypothesise that artificial intelligence (AI), machine learning (ML) and deep learning (DL) may offer a transformative approach by integrating multimodal data, including pathological biomarkers, clinical history, and vascular imaging, to enable precision CVD/stroke risk stratification, pending rigorous external validation in sex-stratified cohorts. Method: This narrative review adopts a PRISMA-informed study selection framework and oversees gender-specific biomarkers, including vasoactive peptides (adrenomedullin), adipocytokines (adiponectin), inflammatory mediators (hs-CRP, IL-6), and thrombogenic factors (homocysteine, D-dimer), alongside clinical variables (APOs, autoimmune disorders) and ultrasonographic markers, carotid intima-media thickness (cIMT), plaque burden and plaque area (PA). Advanced ML/DL algorithms were employed to synthesise these heterogeneous datasets, identifying nonlinear interactions for better outcomes. Findings: Key insights reveal that hormonal dynamics (e.g., hypoestrogenism post-menopause) modulate CVD risk, while APOs induce persistent endothelial dysfunction and subclinical atherosclerosis. Biomarker sexual dimorphism is evident; hs-CRP exhibits higher baseline levels in women, whereas adiponectin declines with metabolic dysfunction. Radiomic features (cIMT progression, plaque morphology) are a well-established biomarker for CVD risk stratification. Conclusions: The integration of AI-driven multimodal systems holds the potential to enable a paradigm shift from population-based to personalised risk assessment, addressing critical gaps in female CVD health. However, this potential is currently at the early validation stage, and widespread clinical implementation requires prospective, externally validated, and ethnically diverse studies. Future applications should incorporate longitudinal biomarker profiling and advanced imaging, namely shear wave elastography and plaque radiomics, to optimise predictive models. Full article

Background/Objectives: Frailty is common among rural Chinese older adults despite relatively high daily physical activity, a phenomenon known as the “rural frailty paradox.” Conventional moderate-to-vigorous physical activity (MVPA) metrics rely on absolute cut-points and are often highly correlated with activity volume, limiting their ability to distinguish the roles of activity volume and activity intensity distribution. We therefore applied a cut-point-free accelerometer approach using average acceleration (AvAcc) and intensity gradient (IG) to distinguish activity volume from activity intensity distribution and to examine whether activity intensity distribution, together with diet quality, could help explain the rural frailty paradox beyond total activity volume alone. Methods: In this cross-sectional analysis of the Healthy Aging and Lifestyle Enhancement study, 1203 rural older adults were included. Physical activity (PA) was objectively measured using triaxial accelerometers to derive AvAcc and the IG. Diet quality was assessed using the China Prime Diet Quality Score (CPDQS), and frailty was assessed using the Fried frailty phenotype adapted for rural Chinese older adults. Multiple linear regression, joint effect models, and restricted cubic spline analyses were conducted after adjustment for age, sex, chronic disease status, total energy intake, and related covariates. Results: In mutually adjusted models, higher IG and CPDQS were independently associated with lower frailty scores, whereas AvAcc was not. In the fully adjusted model, IG (β = −0.14, p < 0.001) and CPDQS (β = −0.10, p < 0.001) were inversely associated with frailty score, while AvAcc showed no significant association (p = 0.665). In joint analyses, compared with the low-IG/low-CPDQS group, participants with high IG/high CPDQS had the lowest frailty scores (β = −0.28, p < 0.001), followed by those with low IG/high CPDQS (β = −0.20, p = 0.002). Restricted cubic spline analyses indicated a non-linear association between IG and frailty and an approximately linear inverse association for CPDQS. Conclusions: These findings suggest that, among rural older adults, frailty may be more strongly associated with activity intensity distribution than with total activity volume alone. Together with diet quality, this may help explain the rural frailty paradox. Full article

Deep soft-rock tunnels are prone to large deformations and structural damage. This study used the Guanyinping Tunnel as a prototype and conducted 1/50-scale progressive loading model tests under three support configurations: rock-bolt-free, equal-length rock bolts, and mixed long–short rock bolts. Rock stress, radial rock displacement (u), and rock bolt axial force (F_N) at the vault, arch shoulders, sidewalls, and wall feet were monitored to reveal reinforcement mechanisms and mechanical response. The results indicated that stress evolution in the bolt-free case exhibited significant spatial heterogeneity. The vault experienced horizontal stress concentration, while the arch shoulder underwent vertical stress concentration. u underwent a three-stage nonlinear progression: elastic linear growth, plastic linear growth, and plastic-accelerated growth. Displacement at the vault was markedly larger than that at other locations. Equal-length rock bolts substantially improved the rock mass stability by delaying stress concentration and fracture propagation. This reinforcement raised the elastic response threshold to 96 kPa and substantially reduced u. F_N at the vault and shoulder followed linear growth, accelerated growth, and then gradual decline, whereas F_N at the sidewalls and wall feet exhibited a steady linear trend. Combined long and short rock bolts produced a multi-level anchoring effect. Short bolts induced a shallow arching action, while long bolts provided deep suspension. This synergy raised the elastic response threshold to a maximum of 120 kPa and moderated the stress reduction process. Deep residual stresses increased to 74.3–88.4% of peak values. The displacement gradient between shallow and deep rock masses was significantly reduced. The coordinated deformation capacity within the anchoring zone was markedly enhanced. F_N distribution exhibited spatial differentiation: short bolts carried the load initially, followed by the activation of long bolts. Both anchoring schemes increased residual stress and mitigated rock mass deformation. The deformation control effect was stronger in shallow rock mass than in deep rock mass. Improvements at the vault and arch shoulders exceeded those at the sidewalls and wall feet. The mixed short–long bolt configuration was superior because it maximized the self-bearing capacity of the deep rock mass. The findings provide experimental data and theoretical guidance for the design and optimization of rock-bolt support in deep soft-rock tunnels. Full article

Micro- and nano-plastic pollution caused by the mismanagement of plastics waste is a significant problem worldwide, causing severe impacts in aquatic and terrestrial environments. The purpose of this study was to evaluate the adsorption capacity of a thermally stable and superhydrophobic bio-substrate to remove microplastic particles (MPPs) from aqueous systems. In this work, the adsorption efficiency of cattail fluff towards MPPs from pristine PP, PVC, PA6, LDPE, HDPE, and their blend was evaluated. The effect of temperature (30 °C, 40 °C, and 50 °C) and two binding environments (distilled water and industrial wastewater) on adsorption was determined. Non-linear regressions of seven adsorption isotherm models including Langmuir, Freundlich, Temkin, Dubinin–Radushkevich (D–R), Redlich–Peterson (R–P), Toth, and Sips were applied to fit the experimental data. Error function analysis confirmed that the D–R adsorption isotherm model offers the best fit of the experimental data. The results show that the bio-substrate is very effective in adsorbing MPPs from aqueous systems with adsorption capacities of q_e = 3597 mg/g and q_e = 2807 mg/g in distilled water and synthetic industrial water, respectively. The composition of the MPPs determines the effect of temperature and binding environment on the adsorption performance of the bio-substrate. Physisorption dynamics for the MPP/bio-substrate system are also provided and discussed. Overall, the hydrophobic bio-substrate is highly effective in removing MPPs from aqueous systems, with the added advantages of low cost, sustainability, and scalability for practical applications. Full article

Global Navigation Satellite System Reflectometry (GNSS-R) provides a promising approach for global ocean wind monitoring with high temporal resolution. However, accurate wind speed retrieval remains challenging due to the complex scattering mechanisms and the nonlinear coupling between delay–Doppler maps (DDMs) and observation geometries. To address these limitations, a Physics-Aware Hybrid CNN–Transformer Network (PA-HCTN) is proposed accordingly. The model integrates a CNN for local DDM feature extraction, a Transformer encoder for global context modeling, and a cross-attention module to dynamically fuse auxiliary physical parameters. A geophysical model function (GMF)-constrained loss is incorporated to enhance physical consistency. Evaluated on CYGNSS and ERA5 data, the PA-HCTN achieves an RMSE of 1.35 m/s and an R² of 0.75, outperforming existing benchmarks and significantly mitigating high-wind-speed underestimation. In addition, through independent validation using NDBC buoy data from four sites, the results demonstrate the effectiveness of the hybrid architecture and physics-aware design for GNSS-R wind retrieval. Full article

Demand forecasting is crucial for optimizing cross-border e-commerce operations, yet traditional methods often struggle to capture complex input–output relationships and nonlinear patterns. This paper proposes an enhanced model, Particle Swarm Optimization with Attention and Strategy (PAS), to address the low search accuracy and slow convergence of conventional PSO. An optimal-point set strategy is introduced to improve population initialization and global search efficiency, enabling more effective global and local exploration. Moreover, an improved Transformer model is adapted for demand forecasting by separately modeling input and output features and fusing them through the decoder, allowing the model to better capture complex relationships between e-commerce variables. A multi-stage search and learning mechanism is further designed, in which PSO first explores the global demand space, followed by localized learning using attention mechanisms. This staged process accelerates convergence and reduces the risk of falling into local optima. Furthermore, we also conducted comparative experiments on the proposed PSO algorithm with two classical optimization algorithms, including the genetic algorithm (GA) and simulated annealing (SA), to demonstrate the rationality of the proposed method. Evaluation on real-world datasets shows that the proposed model markedly surpasses conventional approaches, achieving an average MAPE of 8.7%, which is 23% lower than the Transformer model and 30% lower than the LSTM model. This has certain significance for the reliability and stability of demand forecasting in e-commerce. Full article

Carbon nanotube yarns (CNTYs) have received more consideration recently due to their excellent specific mechanical, electrical and thermal properties, making them promising materials for different applications. Until now, the axial properties of the yarn have been thoroughly investigated; however, the transverse or radial properties, orthogonal to the fiber axis, remain relatively unknown due to the challenges associated with their measurement. In this study, the transverse or radial response of the CNTY including its elastic modulus was determined using Atomic Force Microscopy (AFM) and Raman Spectroscopy. Determining transverse properties in fibrous materials presents challenges owing to their geometry, inherent anisotropy, whereby mechanical characteristics exhibit directional disparities; i.e., the properties in the transverse direction may be several orders of magnitude smaller than those in the axial direction. To overcome these difficulties, AFM was utilized to perform nanoindentation experiments, where a tipless flexible cantilever probe was used to apply a controlled force to the CNTY surface. The resulting indentation depth was then analyzed to determine the transversal elastic modulus. Preliminary findings indicate that the transverse elastic modulus of the CNTYs ranges from 10–54 kPa for strain levels below 3%. Complementary Raman spectroscopy provided insight into the bulk-scale mechanical behavior of CNTYs. Incremental compressive loading between microscope slides induced nonlinear upshifts in the 2D Raman band (from ~2686.6 to 2691.4 cm⁻¹), indicating nanoscale tube realignment, inter-tube densification, and compaction. From lateral diameter measurements under load, a stress–strain curve was constructed, revealing three distinct regimes: one with an initial elastic modulus of 3.12 MPa (0.3–11.2% strain), another one with an elastic modulus increasing to 8.46 MPa (11.2–14.4%), and finally one with an elastic modulus peaking at 16.86 MPa beyond 14.4% strain. Together, these methods delineate the hierarchical and anisotropic nature of CNTYs, validating the importance of multiscale mechanical characterization for their deployment in piezoresistive sensors and multifunctional composites. This study establishes a robust framework for quantifying the transverse mechanical response of CNTYs. Full article

In the face of the dual challenges of global climate change and rapid urbanization, optimizing the ecosystem services of urban green spaces has become a key strategy for building resilient and sustainable cities. This is particularly crucial in ecologically fragile arid and semi-arid regions. To accurately assess the thermal regulation function of urban green spaces, this study selected 20 parks in Xi’an, China. Combining remote sensing and Geographic Information System (GIS) technology, we adopted four established cooling indicators—Park Cooling Area (PCA), Park Cooling Efficiency (PCE), Park Cooling Intensity (PCI), and Park Cooling Gradient (PCG)—to systematically evaluate the thermal regulation functions of urban parks and their landscape-driving mechanisms. The results indicated that the average cooling amplitude of the parks was 2.53 °C, with an effective influence distance reaching 323.9 m, exhibiting a significant spatial gradient decay. We found a non-linear trade-off between green space scale and efficiency: while large parks provided a wider absolute cooling range, small and medium-sized parks demonstrated higher efficiency per unit area. Furthermore, a blue-green synergistic configuration significantly enhanced the mitigation of the urban heat island effect. The study confirmed that Park Area (PA), Park Perimeter (PP), and the Normalized Difference Vegetation Index (NDVI) significantly promoted cooling effects, whereas landscape fragmentation inhibited ecological benefits. This study elucidates the comprehensive regulation mechanism of urban parks on the urban microclimate, providing planning guidance for implementing Nature-based Solutions (NbS) and achieving climate-adaptive development in arid and semi-arid cities within the context of urban renewal. Full article

The Loess Plateau has experienced persistent vegetation greening over the past two decades, yet this recovery has occurred under a concurrent intensification of atmospheric evaporative demand and drying. This raises a key land–atmosphere question: which hydroclimatic processes most strongly constrain greening, and where do vegetation responses shift across environmental regimes? To address this issue, we integrated spatiotemporal trend analysis, Geographical Convergent Cross Mapping (GCCM)-based directional attribution, and an interpretable machine-learning framework combining Extreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP) to diagnose the dominant controls and threshold-like response patterns of vegetation activity. Using 1 km kernel Normalized Difference Vegetation Index (kNDVI) and eight hydroclimatic variables during 2000–2019, we found that regionally averaged kNDVI increased from 0.099 in 2000 to 0.164 in 2019, with a significant trend of 0.003 year⁻¹, and greening trends covered 65.503% of the Loess Plateau. Over the same period, Vapor Pressure Deficit (VPD) increased from 0.142 to 0.275 kPa (+0.133 kPa), indicating that vegetation recovery did not occur under a more humid atmospheric background. GCCM results consistently showed stronger directional influence from hydroclimatic drivers to kNDVI than the reverse, with evaporation and thermal conditions, especially Tmin, emerging as the dominant constraints, followed by Tmax, VPD, and wind speed, whereas precipitation showed comparatively weaker recoverable influence. The tuned XGBoost model achieved strong out-of-sample performance (R² = 0.9611, RMSE = 0.0188, MAE = 0.0131), and SHAP revealed clear nonlinear thresholds: evaporation and Tmin shifted into persistently positive contribution regimes beyond 302 mm and −17.6 °C, respectively; Tmax became predominantly inhibitory beyond −1.9 °C, and Palmer Drought Severity Index (PDSI) exhibited a multi-stage non-monotonic transition around −0.7. These results provide a coherent evidence chain linking directional influence, relative contribution, and threshold boundaries, offering quantitative support for identifying climate-sensitive zones and restoration risk regimes under continued warming and rising atmospheric dryness. Full article

Dynamic characterization of calcareous (carbonate) sands is essential for performance-based design of offshore foundations, coastal reclamation, and marine infrastructure in tropical and subtropical regions. In contrast to silica sands, carbonate sediments are biogenic and typically comprise angular, irregular grains with intra-particle voids and fragile skeletal microstructure. These traits promote grain crushing and fabric evolution at relatively low-to-moderate confinement, leading to pronounced stress dependency, strong nonlinearity with strain amplitude, and substantial scatter in laboratory stiffness and damping measurements. Consequently, empirical correlations calibrated primarily on quartz sands may yield biased estimates when transferred to carbonate environments. This study presents an ML-driven, leakage-aware benchmarking framework for predicting two key dynamic parameters of biogenic calcareous sands, damping ratio $D$ and shear modulus $G$, using standard tabular descriptors commonly available in geotechnical practice. Two consolidated experimental databases were curated from resonant column and cyclic triaxial measurements ($D$: $n=890$; $G$: $n=966$), spanning mean effective confining stress $25 \le {\sigma }_m^{\prime }\le 1600 \mathrm{k}$Pa and a wide range of density and gradation conditions. To emphasize transferability, explicit deposit/site labels were excluded, and missingness arising from heterogeneous reporting was handled through a consistent preprocessing pipeline (training-only imputation, categorical encoding, and scaling). Eleven regression algorithms were evaluated, covering linear baselines, regularized regression, neighborhood learning, single trees, bagging and boosting ensembles, kernel regression, and a feedforward neural network. Performance was assessed using $R^2$, RMSE, and MAE on training/validation/test splits, and engineering credibility was supported through explainability-based diagnostics to verify mechanically plausible sensitivities. Results show that ensemble-tree models (Extra Trees and Random Forest) provide the most reliable accuracy–robustness balance across both targets, consistently outperforming linear models and the tested SVR configuration and exhibiting stable validation-to-test behavior. The explainability audit confirms physically meaningful separation of governing controls: stiffness is primarily stress-controlled (${\sigma }_m^{\prime }$ dominant for $G$), whereas damping is primarily strain-controlled ($\gamma$ dominant for $D$). The proposed framework supports practical deployment as a fast surrogate for generating $G\hspace{0.17em}-\hspace{0.17em}\gamma$ and $D\hspace{0.17em}-\hspace{0.17em}\gamma$ curves within the training domain and for guiding targeted laboratory test planning in carbonate settings. Full article

Soil–steel interface shear governs load transfer and long-term serviceability in piles, retaining systems, and buried infrastructure; yet the large-displacement interface mechanics of fibre-reinforced sands remain poorly resolved, limiting sustainable design. This study couples large-displacement ring-shear testing with physics-guided hybrid AI to quantify and predict the peak and residual resistance of sand–polypropylene fibre mixtures sliding on smooth and rough steel. Two quartz sands with contrasting particle morphology were tested under 25–200 kPa normal stress and 0–1.0% fibre content, producing a design-oriented database that captures post-peak evolution and residual states. The experiments reveal a strongly nonlinear reinforcement law: an optimum fibre range enhances dilation, stabilises the shear band, suppresses post-peak softening, and increases residual strength, whereas excessive fibres disrupt the granular skeleton and reduce mobilisation efficiency. Roughness and confinement act as amplifiers, intensifying fibre-driven dilation and asperity interlock. To translate mechanisms into prediction, three strategies were benchmarked: a deep neural network (DNN), the Physics-Guided Neural Additive Model (PG-NAM++), and the physics-anchored Residual-DNN that learns only the correction to a mechanical baseline. Residual-DNN achieved the tightest agreement and the highest physical consistency for both peak and residual strength, enabling robust parameter selection with reduced uncertainty and overdesign. The combined experimental–AI framework advances the United Nations Sustainable Development Goals (SDGs) by supporting SDG 9 through resilient, innovation-led infrastructure design and contributing to SDG 12 by enabling optimised (rather than maximal) use and reuse of reinforcement materials within circular ground-improvement practice. Full article

More and more spaceborne infrared hyperspectral atmospheric observations are assimilated into data assimilation systems. The key to bias correction (BC) of these instruments depends on selecting predictors. However, it is difficult to find a set of predictors that are highly correlated with the O-B biases in all FY-3E/HIRAS-II channels, due to its multi-channel characteristics. A machine learning model XGBoost (EXtreme Gradient Boosting) BC scheme for FY-3E/HIRAS-II is established in this article. The selected predictors include model skin temperature, model total column water vapor, 1000–300 hPa thickness, 200–50 hPa thickness, scan position, observed brightness temperature (BT) and simulated BT. The method is also compared with the operational static BC and the variational BC, to validate its effect. The two-week data assimilation experiments show that the XGBoost BC is the most effective among the three BC schemes. The mean and standard deviation of O-B in all channels are the smallest after BC, and the effective observations through quality control are the largest, followed by the static BC. The static BC and variational BC are performed based on linear regression, which may lead to a small loss of valid observations in some channels that are weakly correlated with the predictor, whereas machine learning algorithms can search for the nonlinear correlation between biases and predictors. Compared with ERA5, both temperature- and humidity-analysis fields based on XGBoost BC are closest to ERA5 at all levels, and the root mean square errors do not change much over time. Full article

This study presents a case-specific joining method for modular, large-scale components manufactured using Selective Laser Sintering (SLS). A T-slot joint reinforced with a pultruded carbon fiber rod was developed to enable the segmental assembly of polymer fan blades that exceed the build volume of common SLS printers. Through an iterative design process, five joint variations were investigated, focusing on the optimization of slot geometry (fillet radii and wall thickness) and the integration of carbon fiber reinforcements to create a high-strength hybrid connection. The experimental findings were validated using a non-linear finite element analysis (FEA) utilizing an iteratively calibrated Young’s modulus of 710 MPa, which accounts for the 50/50 virgin-to-reused PA2200 powder ratio employed in the study. The numerical model identified that the primary sites for crack initiation were the fillet radii of the female slot, where localized equivalent plastic strains reached critical levels of up to 84% in tension and 78% in bending. The final design achieved an average tensile strength of 27.6 MPa, exceeding the design threshold of 21.9 MPa with a safety factor of 2.5. While unreinforced joints showed a 73.4% reduction in bending strength compared to solid specimens, the addition of an 8 mm carbon rod increased performance by 238.7%, restoring over 90% of the monolithic material’s strength. Numerical results confirmed that the reinforcement assumed the primary load-bearing role, effectively mitigating stresses in the polymer matrix below the ultimate tensile strength. Failure analysis clarified that the observed audible failure originated from internal fiber breakage within the rod at stresses between 900–1050 MPa. This work demonstrates that a segmental, reinforcement-based joining method can effectively overcome size constraints in polymer additive manufacturing, providing a robust and repeatable solution for rotating components subject to complex loading conditions. Full article

Ageing significantly affects the long-term durability of asphalt pavements, yet quantitative correlations between laboratory ageing protocols and actual field ageing remain insufficiently defined. This study investigates the ageing behaviour of an 80/100 penetration-grade bitumen at binder, mixture, and field levels to establish equivalence relationships among different ageing pathways. Binder samples were subjected to RTFO, PAV (20–60 h), and coupled thermal–photo-oxidative ageing (RTFO + PAV + UV, 6–18 d). Asphalt mixtures were oven-aged at 85 °C for 5–10 d, followed by binder extraction and recovery, and field-aged binders were obtained from a 12-year-old pavement in Xinjiang, China. Rheological properties were characterised using frequency sweep and multiple stress creep and recovery tests, from which ageing index (AI), low-temperature ageing index (LAI), Glover–Rowe (G–R) parameter, and nonrecoverable compliance (J_nr) were derived. AI increased from 1.00 for virgin binder to 1.12 under coupled ageing, while G–R increased from near zero to 318 kPa after 60 h PAV ageing and exceeded 400 kPa under coupled ageing. UV exposure increased G–R by approximately 20%–65% relative to thermal ageing alone. Nonlinear growth models described property evolution with high reliability (R² = 0.995–0.999). Equivalent ageing analysis showed that RTFO + PAV required over 50 h to reproduce field ageing, whereas coupled ageing and mixture oven ageing achieved comparable states within shorter durations. These results demonstrate that photo-oxidation and mixture-scale interactions significantly influence ageing pathways and should be considered in laboratory simulations of field ageing. Full article