Search Results (423)

The post-anthesis sepal inflation forming a Chinese lantern in Physalis pubescens represents a striking morphological novelty, whereas in the related genus Solanum lycopersicum var. cerasiforme, the sepals remain non-enclosing, providing an ideal comparative system to study reproductive organ divergence. This study aimed to systematically compare the reproductive development between Physalis pubescens L. and Solanum lycopersicum var. cerasiforme. We integrated phylogenetic analysis, light and scanning electron microscopy, semi-thin sectioning, and quantitative RT-qPCR analysis of six MADS-box genes. Phylogenetic analysis placed P. pubescens in a clade with P. alkekengi (L.) and P. ixocarpa (Brot. ex Hornem.), distinct from tomato. Floral organs differed markedly in petal color, anther morphology, and dehiscence type. Microspore development was delayed before the tetrad stage but accelerated thereafter in P. pubescens; S. lycopersicum anthers exhibited pronounced connective tissue proliferation absent in P. pubescens. P. pubescens sepals expanded 6.92-fold by 17 days post anthesis and enclosed the fruit, while S. lycopersicum sepals grew minimally. Sepal expansion was driven by inner epidermal cell enlargement and intercellular space formation, producing a hollow structure with a trichome-free inner epidermis. Expression of six MADS-box genes exhibited diversity: PfMPF2 and PfAGL1 were upregulated during sepal growth, PfMPF3, PfSEP1 and PfSEP3 exhibited a high–low–high pattern with minima at peak growth, and PfAGL6 declined. These findings reveal that coordinated epidermal cell expansion, parenchyma cavity formation, and a dynamic MADS-box gene network govern the inflated sepal syndrome, and highlight key divergences in anther morphogenesis, providing a cellular and molecular framework for reproductive evolution in Solanaceae. Full article

This paper investigates the degradation law and calculation method for the load-carrying capacity of steel–concrete composite girders under fire exposure based on numerical analysis and mathematical statistics. A finite element model of simply supported box-section steel–concrete composite girders is established using ABAQUS, which is validated against existing scaled test data in terms of temperature field distribution, load-carrying capacity, and mid-span displacement. On this basis, the parametric effects of concrete slab thickness, steel web height, steel plate thickness, and concrete strength on the load-carrying capacity of the girders are systematically analyzed. The results indicate that concrete slab thickness, steel web height, and steel plate thickness exert significant influences on the structural bearing capacity, whereas concrete strength has a negligible effect. Specifically, the load-carrying capacity under fire exposure is substantially improved with the increase in concrete slab thickness, steel web height, and steel plate thickness. Furthermore, a simplified calculation formula for the capacity of box-section steel–concrete composite girders under fire exposure is developed via multiple linear regression analysis, incorporating the three dominant influencing factors of concrete slab thickness, steel web height and steel plate thickness. The proposed formula exhibits satisfactory calculation accuracy and can provide a reliable reference for the fire resistance design and repair decision-making of steel–concrete composite girders. Full article

As the retail banking sector shifts toward automated lending, the black-box nature of high-performing machine learning models remains a significant barrier to regulatory transparency and institutional trust. A critical gap in existing literature is the lack of deployed frameworks that simultaneously optimize predictive accuracy, manage asymmetric financial risks, and provide actionable interpretability. To bridge this gap, this study aims to develop and evaluate a highly interpretable, ethically accountable ensemble machine learning framework for credit risk assessment. Utilizing a cross-sectional public dataset of over 45,000 generalized retail banking records, this research conducts a comprehensive comparative analysis of four diverse ensemble architectures: Bagging, Boosting, Stacking, and Voting. To address inherent class imbalance and evaluate risk tolerance, the models were integrated with Synthetic Minority Over-sampling Technique (SMOTE) and Adaptive Synthetic Sampling (ADASYN) resampling techniques. While all architectures demonstrated high discriminative power, the SMOTE-balanced Bagging model emerged as the superior performer, achieving a peak Area Under the Curve (AUC) of 0.972 by establishing a safe operational threshold that strictly minimizes costly false approvals. Crucially, a SHapley Additive exPlanations (SHAP) framework was applied across all four models to decode their internal logic. The SHAP analysis successfully validated that the ensembles prioritize core financial behavior, such as default history and loan-to-income ratios, while correctly assigning near-zero predictive weight to demographic traits like gender and education. By empirically proving that high-performance algorithms can be mathematically blind to demographic biases, this framework directly advances SDG 10 (Reduced Inequalities). Furthermore, by resolving the performance-transparency trade-off, this study provides the accountable, feature-level justifications required for secure and sustainable financial inclusion (SDG 8). Full article

The progressive integration of Whole Slide Imaging (WSI) technology and Artificial Intelligence (AI) architectures is driving a structural transformation in pathology and precision oncology. This structured critical review analyzes and systematizes the impact of this technological transition along two fundamental operational dimensions of the modern biopharmaceutical industry: pre-registration Clinical Research and post-launch strategies governed by Medical Affairs. The first section explores how computational pathology is improving efficiency and reducing risk in drug development. Replacing analog visual assessment—intrinsically subject to inter-observer and intra-observer variability—with quantitative algorithms for cellular classification and segmentation enables optimization of patient recruitment in clinical trials, reducing screening failure rates. This review also examines the emerging role of Spatial Biology in extracting complex topological metrics from the Tumor Microenvironment (TME) and the use of AI for the objective and auditable quantification of critical surrogate endpoints, such as Pathological Complete Response (pCR), while acknowledging that algorithmic precision remains sensitive to pre-analytical variables and dataset biases. In the second section, the study investigates the strategic evolution of Medical Affairs, acting as a vital scientific communication and translational bridge between the complexity of Data Science and clinical hospital practice. Challenges related to AI adoption by clinicians are examined, emphasizing the importance of educational programs based on Explainable AI (XAI) to overcome the cognitive limitations of the black-box paradigm and the complex regulatory validation pathway for Software as a Medical Device (SaMD) under the stringent European IVDR framework—supported by an analysis of historical regulatory benchmarks such as the Paige Prostate case. The paper also explores the potential of AI in the large-scale generation of Real-World Evidence (RWE), applied to the creation of synthetic control arms in pharmacoeconomic settings. In conclusion, the study highlights that the diagnostic algorithm has ceased to be merely a laboratory support tool and has become a strategic asset and an integral adjunct to therapeutic decision-making. Overcoming current challenges related to data privacy through Federated Learning architectures, together with the imminent transition toward Foundation Models, foreshadows a fully data-driven healthcare ecosystem, making continuous skills development (digital upskilling) an essential requirement for professionals in the biopharmaceutical sector. Full article

This study investigates the shear behavior and failure mechanisms of innovative hybrid structural beams fabricated by filling pultruded glass fiber-reinforced polymer (GFRP) box sections with waste rubber-reinforced concrete (RuC). Environmentally friendly concrete was produced by replacing natural aggregate with recycled tire-rubber fibers at proportions of 0%, 5%, 10%, and 15%. Twelve hybrid beam specimens were tested to evaluate the synergistic effects of rubber content and stirrup spacings of 16, 20, and 27 cm on shear capacity, ductility, and crack propagation. The experimental results revealed that the reference specimen (S16-0%) exhibited the maximum shear capacity of 154.41 kN and a brittle failure mode, while an increase in rubber content to 15%, combined with wider stirrup spacing, significantly reduced this capacity to a minimum of 96.89 kN (S27-15%). However, the 5% rubber replacement ratio achieved an optimal performance balance by preserving sufficient load-carrying capacity while enhancing flexural deformation and ductility, particularly in specimens with 16 cm stirrup spacing. Damage analysis demonstrated that longitudinal splitting cracks initiated in the mid-span tension zone at the bottom of the pultruded profiles, with final localized damage concentrated at the geometric corners of the box section. Crucially, the outer pultruded GFRP profiles provided substantial structural confinement, effectively mitigating the strength loss associated with high rubber incorporation and controlling the progression of sudden brittle failure. These findings highlight that combining pultruded GFRP profiles and optimized RuC offers a structurally viable and sustainable solution for modern infrastructure applications. Full article

During the fabrication of prestressed concrete beams, the quality and positional accuracy of the laid corrugated ducts (or prestressing ducts) directly influence the load-bearing capacity and durability of the beams. However, traditional manual inspection is inefficient, highly subjective, and difficult to achieve full coverage. To address this problem, this paper proposes an automated detection method that integrates improved YOLOv11-based pose estimation, robust curve fitting, and image stitching techniques. The method automatically identifies duct positions and evaluates laying quality. By incorporating the SE channel attention mechanism and the SPPFCSPC multi-scale pooling module, the SC-YOLOv11 model is developed, which significantly enhances the detection accuracy of slender corrugated pipe key points in environments with dense rebar occlusion. The RANSAC algorithm is employed to fit curves to the predicted key points, effectively suppressing the influence of outliers. Furthermore, the SIFT algorithm is used for precise stitching of drone-captured segmented images, which are then transformed into a unified front orthographic coordinate system of the entire box girder via perspective transformation, enabling accurate reconstruction of the corrected 2D layout of corrugated ducts across the full beam. Ablation experiments using 5-fold cross-validation demonstrate that SC-YOLOv11 improves mAP₅₀ and mAP_50–95 by 2.6% and 1.2%, respectively, with statistical significance (paired t-test, p < 0.01). The model achieves a per-image inference time of 6.37 ms, with 4.34 M parameters and 8.1 GFLOPs, meeting real-time requirements. In a 30 m prefabricated box girder field application, the measured section trajectory fitting curves of the corrugated ducts were compared with the design alignment, successfully identifying two abnormal locations where the laying deviation exceeded the allowable threshold. Cross-validation with on-site inspector records shows that over 92% of the measurement points agree within ±10 mm. This method achieves a fully automated analysis chain from key point detection and curve fitting to deviation quantification, providing an efficient, non-contact, and traceable intelligent tool for quality control of bridge prestressed systems. Full article

Blending hydrogen into existing natural-gas pipeline networks provides a practicable route toward future low-carbon applications. A Venturi-type mixer is a classical high-efficiency static gas-mixing device, and clarifying the effects of its structural parameters is important for efficient transport and downstream combustion stability. In this study, numerical simulations were performed in ANSYS Fluent 2024 R1. The contraction angle, throat length, and diffuser angle were selected as representative structural variables. First, the independent effects of these variables on the mixing process were examined through single-factor simulations. Then, three key levels of the three structural parameters were selected to establish a Box–Behnken experimental matrix for response-surface modeling. Based on the numerical results, entropy weighting and a genetic algorithm were used for multi-objective optimization, and the final solution was verified using the TOPSIS method. The results show that the optimized Venturi-type mixing device with optimized parameters of a contraction angle of 20.7°, a throat length of 60 mm, and a diffuser angle of 5° can reduce flow energy loss while maintaining high mixing uniformity. The diffuser angle is the dominant geometric parameter affecting both energy loss and mixing behavior. Compared with the reference central-point structure design, the overall TOPSIS score of the optimized structure increased from 0.41 to 0.82; the pressure loss decreased from 258.94 Pa to 206 Pa, corresponding to a reduction of approximately 20%; and the final-section mixing uniformity decreased only slightly, from 97.85% to 97.43%. Full article

Background: Anti-Müllerian hormone (AMH) is widely used as a marker of ovarian reserve and is strongly influenced by chronological age. Although inflammation has been implicated in ovarian aging and follicular dysfunction, whether routinely measured peripheral inflammatory markers provide additional information regarding AMH concentrations remains unclear. Objective: This study aimed to evaluate the age-adjusted associations of C-reactive protein (CRP) and the neutrophil-to-lymphocyte ratio (NLR) with serum AMH concentrations in reproductive-age women. Methods: This retrospective cross-sectional study included women aged 18–45 years with available AMH, complete blood count, and CRP measurements. After reapplying the prespecified eligibility criteria and excluding confirmed data-entry or analytical errors, 819 women were included in the final analysis. NLR was calculated from absolute neutrophil and lymphocyte counts. Because CRP and NLR showed right-skewed distributions, Box–Cox transformations were applied. Associations with Box–Cox-transformed AMH were evaluated using an age-adjusted left-censored Tobit regression model. Results: The mean age of the participants was 34.07 ± 6.94 years, and the median AMH concentration was 1.07 ng/mL (interquartile range, 0.29–2.50). Chronological age was inversely associated with AMH (β = −0.123, 95% CI: −0.135 to −0.111, p < 0.001). After adjustment for age, neither CRP (β = −0.038, 95% CI: −0.085 to 0.009, p = 0.114) nor NLR (β = −0.012, 95% CI: −0.222 to 0.198, p = 0.912) was independently associated with AMH. Conclusions: In this retrospective outpatient cohort, routine peripheral inflammatory markers did not explain additional variation in AMH beyond chronological age. These results do not exclude a potential role for local ovarian inflammation, which may not be adequately captured by peripheral CRP or NLR measurements. Full article

This paper presents an integrated framework for automated detection of surface defects in infrastructures using unmanned aerial vehicles (UAVs), comprising 3D model-based adaptive path planning, high-resolution image acquisition, and an attention-enhanced instance segmentation model. However, existing approaches face two key limitations: (i) conventional UAV path planning lacks adaptive trajectory correction for non-horizontal bridge geometries, and (ii) instance segmentation models for infrastructure defects have not been systematically optimized for both accuracy and edge-device deployability. To address these gaps, the proposed framework was trained on 3625 annotated images covering two defect categories (spalling and cracking) and preliminarily validated through a proof-of-concept field study on a concrete viaduct section where seven spalling and one reinforcement exposure were detected. The methodology consists of three core components: (i) 3D model-based adaptive path planning, (ii) high-resolution image acquisition under variable infrastructure geometries, and (iii) an improved instance segmentation model based on YOLOv8-seg. To ensure consistent imaging geometry, a segmented linear interpolation method is introduced to adaptively correct flight trajectories for non-horizontal infrastructure sections. For damage detection, we propose a structurally enhanced YOLOv8-seg model, denoted as YOLOv8-seg-ECAC2f-all, which integrates Efficient Channel Attention (ECA) modules into a fully modified backbone architecture. Compared to the baseline YOLOv8-seg, the proposed model achieves a mean average precision (mAP_50–95) of 91.8% for bounding box detection and 61.7% for instance segmentation, corresponding to improvements of 7.7% and 2.8%, respectively. The framework was preliminarily validated on a concrete viaduct of the Guangfojiangzhu Expressway, achieving 100% inspection coverage and detection of all eight ground-truth surface defects (seven spalling and one reinforcement exposure) in this pilot study, including two minor spalling cases (≤0.5 m²) missed by manual inspection. These results demonstrate the technical feasibility of the proposed framework for real-world concrete bridge inspection and its potential for reducing manual inspection effort while improving detection sensitivity for minor defects (${\mathrm{m}\mathrm{A}\mathrm{P}}_{50–95}$). Full article

Vortex-induced vibration (VIV) poses a significant serviceability concern for wide streamlined box girders of long-span suspension bridges. This study investigates the VIV performance and aerodynamic mitigation of a wide streamlined box girder with a width-to-depth ratio (B/D) of approximately 10 through sectional model wind tunnel testing. The original cross-section was found to exhibit pronounced heaving and torsional VIV at positive wind angles of attack, with amplitudes considerably exceeding the prescribed serviceability limits. A systematic experimental investigation was conducted to evaluate the influence of three geometric parameters, i.e., wind fairing inclination angle, inspection rail position, and pedestrian railing porosity and panel arrangement, on VIV performance. Experimental results demonstrate that reducing the wind fairing inclination angle from 65° to 45° is the most effective mitigation measure. An appropriate porosity of the pedestrian railing is shown to substantially improve VIV performance. Furthermore, under equivalent overall porosity, a uniformly distributed alternation of solid and ventilated panels yields markedly superior VIV suppression compared with continuously sealed arrangements. Subsequent flutter and aerostatic wind tunnel tests confirm that the recommended cross-section preserves the favorable flutter stability and aerostatic performance of the original design. Strouhal number analysis reveals that the VIV lock-in is governed by St ≈ 0.12. Notably, the St number obtained from the pitching moment coefficient is nearly twice that obtained from the lift coefficient. Full article

Automotive millimeter-wave radar produces sparse point clouds with Doppler velocity and radar cross-section (RCS), but graph detectors typically use a shared representation for semantic prediction and box regression despite their different propagation requirements. We propose multi-GSO spectral filtering (MGSF), a residual module that filters radar features over geometry-, Doppler-, and RCS-defined graph shift operators and fuses diffusion and residual components with a node-adaptive gate. MGSF-TD applies full multi-GSO refinement to semantic prediction and geometry-only refinement to box regression. On the complete RadarScenes validation set, MGSF-TD improves the official RadarGNN checkpoint from 60.19 to 60.59 mAP and from 74.06 to 75.10 mean foreground F1 (FG-F1). Across three MGSF-TD training seeds, the FG-F1 margin under RCS noise increases from $+1.15$ at 3 dBsm to $+2.38$ at 20 dBsm; seed-42 full-validation mAP margins are $+0.24$, $+1.07$, and $+1.82$. Controls show that geometry-only diffusion explains part of the gain and the RCS operator contributes most clearly at low-to-moderate noise, whereas a parameter-matched widened baseline matches or exceeds MGSF-TD under severe RCS and Doppler corruption. Cross-sensor diagnostics reproduce the Doppler failure trend but not the severity-dependent RCS gain. MGSF-TD therefore offers a balanced, physically interpretable operating point rather than a universal robustness gain. Full article

Section-load calibration is used in aircraft wing-box testing. This study evaluates a fixed-position quasi-static load-calibration and identification procedure for one 7050 aluminum wing-box test section instrumented with surface-bonded fiber Bragg grating (FBG) sensors. A multi-point FBG network was arranged on the skins and webs using finite-element-guided sensor placement to construct bending-, shear-, and torsion-related response features; strain-free reference FBGs provided temperature compensation. All experiments used the same specimen geometry, fixed-root boundary condition, sensor layout, and four actuator positions. Conditions 1–6 were used for regression calibration, whereas Conditions 7 and 8 were held out for interpolation-type validation within the same loading configuration. The maximum/average relative errors were 6.53%/1.51% for bending moment, 2.62%/0.86% for shear force, and 4.04%/1.23% for torsional moment. These results apply only to local laboratory calibration of the tested configuration and do not establish transfer to other geometries, boundary conditions, sensor layouts, loading positions, environmental conditions, or dynamic loading. Full article

To simultaneously suppress shrinkage-related defects and refine the solidification microstructure of Mn70Ni25Cr5 alloy ingots, ProCAST simulation was combined with Box–Behnken response surface methodology to optimize pouring temperature, filling time, and mold temperature. Porosity in the ingot body and secondary dendrite arm spacing (SDAS) were selected as the response variables, and quadratic regression models were established for both responses. The optimized casting parameters were determined using analysis of variance, response surface analysis, and the desirability function approach. The porosity and SDAS models were both statistically significant, with non-significant lack-of-fit terms and R² values of 0.9906 and 0.9901, respectively. The optimal parameters were a pouring temperature of 1220.74 °C, a filling time of 6.34 s, and a mold temperature of 294.47 °C, corresponding to a predicted porosity of 0.426% and a predicted SDAS of 47.51 μm. A supplementary simulation and a validation casting experiment were then performed using practical process settings derived from the optimized solution. The supplementary simulation indicated that shrinkage-related defects were concentrated mainly in the riser, while metallographic examination revealed no large continuous shrinkage-porosity region in the examined ingot-body sections. The overall measured SDAS across the center, half-radius, and edge positions was 48.54 μm, differing from the response-surface prediction by approximately 2.2%. These results support the applicability of the combined ProCAST–RSM approach for simulation-assisted optimization of Mn70Ni25Cr5 alloy casting parameters within the investigated process range. Full article

Construction-stage bridge fires are seldom documented in detail, although they can change the behavior of an incomplete structural system. This paper records a 35 m span of a six-cell RC box girder at the Al-Sadreen intersection in Samawa, Iraq, damaged after the bottom slab and webs had been cast and before the top slab was completed. Burning timber formwork locally removed the temporary soffit support. The open-top section, therefore, shifted from the intended fixed–pin construction-stage response toward a pin–pin-like response, with sagging and vertical web cracks near the intended fixed support. A closed-form check showed that the required negative-restraint moment was about 5.3–5.9 times the cracking moment of the incomplete section. Visual inspection, Schmidt hammer, UPV, cores, and steel tests showed localized damage; 28 MPa was used as a representative residual concrete strength for the affected cast components. CSiBridge was used only for completed rehabilitated-state verification. The strengthened model gave maximum shear D/C ≈ 0.529 and flexural D/C < 1.0. Spreadsheet-guided jacking, top-slab reinforcement upgrading, sensitivity checks, and a 350-ton five-lane load test confirmed satisfactory service behavior and negligible residual response. Full article

Driven by the low-carbon transformation in the construction industry, Moso bamboo has emerged as a promising green material. However, a critical gap exists in current structural design theories: they predominantly rely on homogeneous assumptions, failing to capture the inherent spatial variability and coupled strength–stiffness degradation of bamboo. Experimental results reveal a distinct longitudinal gradient, where the top section (5–8 m) exhibits approximately 15% higher average tensile strength and 12% higher average elastic modulus compared to the bottom section (1–3 m). This oversight severely compromises the accuracy of structural reliability evaluations. To address this, this study pioneers a high-precision digital representation method by developing a novel three-dimensional (3D) anisotropic bivariate coupled random field model. Experimental and stochastic finite element simulations demonstrate that the model accurately replicates spatial variations, maintaining relative errors for primary statistical indicators strictly below 1% while robustly capturing the bivariate coupling characteristics. Crucially, by integrating non-parametric probability box (P-box) theory, the macroscopic tensile resistance of full-scale bamboo members is rigorously bounded within a definitive statistical interval. Furthermore, the extracted Interval Skewness Ratio generally remains greater than 1.0 (averaging 1.38), providing robust quantitative proof of an asymmetric structural degradation governed by the brittle weakest-link failure mechanism. By effectively eliminating non-physical sample generation associated with traditional univariate models, this research makes a significant contribution to the field, providing a rigorous digital twin framework and theoretical foundation for the advanced non-probabilistic safety design of modern bamboo structures. Full article