Search Results (5,086)

Analyzing the long-term evolution of meso-scale urban morphological types is constrained by the “small-sample, high-dimensional” nature of historical data, which weakens the robustness and interpretability of conventional data-driven methods and limits the use of morphological evidence in digital urban analysis and built environment governance. To address this, we propose a theory-guided modular principal component analysis (TG-MPCA) framework for sample-constrained morphological research. By embedding domain knowledge into dimensionality reduction, the framework extracts compact, low-dimensional morphological indices that are both morphologically interpretable and internally stable within the selected sample set. Applied to Harbin’s representative districts through unsupervised hierarchical clustering, evolutionary lineage analysis, and transition node identification, it reveals a “layered response pattern” among morphological modules, an asymmetrical mapping between morphological types and historical periods, a structural breakpoint around 1946 that parallels post-war Western urban restructuring, and anomalous deceleration and premature convergence in typological evolution during transitional periods. These observations offer a meso-scale morphological perspective for understanding both the spatial transformation of Chinese cities and the developmental challenges of Northeast China’s old industrial bases, while demonstrating the value of theory-guided quantitative analysis for transforming fragmented historical spatial information into interpretable morphological evidence in data-constrained contexts. Full article

Objective: The inferior mesenteric vein (IMV) is an important component of the portal venous system; however, normative developmental data during childhood remain limited. This study aimed to evaluate age- and sex-related changes in IMV caliber using contrast-enhanced computed tomography in a pediatric population. Methods: Radiologic images of 200 subjects aged 1–20 years were included in the examination. Terminal (IMV1), intermediate (IMV2), and pelvic (IMV3) calibers of the IMV were measured. Ratios of vessel calibers to the transverse diameter of the first lumbar vertebral body (L1TD) were calculated. In addition, the drainage pattern of the IMV was recorded. Results: L1TD, IMV1, IMV2, and IMV3 demonstrated significant age-related changes (p < 0.001). All IMV calibers increased from infancy to the postpubescent period. Among the calculated ratios, IMV1/L1TD and IMV3/L1TD demonstrated significant age-related variation (p < 0.05), whereas IMV2/L1TD remained relatively stable throughout childhood and adolescence (p > 0.05). No significant sex-related differences were observed for most parameters, although IMV3 was significantly greater in males than in females (p = 0.043). The splenic vein was the most common termination site of the IMV (47.5%), followed by the superior mesenteric vein (29.5%) and the splenomesenteric confluence (23.0%). Conclusions: The present study provides normative age-specific values for IMV caliber and IMV-to-L1TD ratios in the pediatric population. These data may facilitate the radiological assessment of IMV-related abnormalities and provide a practical reference for routine computed tomography evaluation of the pediatric mesentericoportal venous system. Full article

Developing white organic light-emitting diodes (WOLEDs) with high exciton utilization, balanced charge transport, and stable complementary emission remains a challenge for solid-state lighting and display applications. Herein, a triplet reverse intersystem crossing (RISC)-assisted strategy is proposed to enhance triplet exciton harvesting to construct efficient hybrid WOLEDs. The increased RISC channels promote the up-conversion of triplet excitons into radiative singlet excitons, thereby improving the overall exciton utilization efficiency. By further introducing an ultrathin PO-01 layer as an orange orange-emitting component, a hybrid WOLED with a current efficiency of 49.1 cd/A and 34.8 lm/W is realized. Moreover, suppressed efficiency roll-offs and stable spectra are achieved due to balanced charge transport. This work provides a practical route toward high-performance WOLEDs. Full article

This study examines the relationship between corporate social responsibility (CSR) and financial performance (FP), measured by return on equity (ROE), and evaluates the moderating role of intellectual capital components, human capital efficiency (HCE), structural capital efficiency (SCE), and capital employed efficiency (CEE), in Philippine-listed firms. A longitudinal panel design was employed using 138 firm-year observations from Philippine Stock Exchange Index (PSEi) companies from 2019 to 2024. CSR was measured through an Environmental, Social, and Governance (ESG) disclosure index based on sustainability and governance reports. Intellectual capital (IC) was operationalized using the value-added intellectual capital (VAIC) framework. Hierarchical moderated panel regression was conducted, with robustness checks using fixed- and random-effects models, Hausman tests, and cluster-robust standard errors. CSR shows a negative but non-robust association with ROE, indicating short-term cost implications in an emerging market context. Among IC components, although CEE exhibited the largest interaction effect in the pooled regression models, its moderating effect was not supported by the panel robustness analyses. HCE shows a positive but less stable effect, while SCE is not significant. Moderation results are limited; HCE weakens the negative CSR–ROE relationship in baseline models but loses significance under robust estimation. CEE shows inconsistent moderation, and SCE has no moderating effect. Disaggregated analysis indicates that environmental and social disclosure dimensions drive the negative association between ESG disclosure and ROE., whereas governance is neutral to slightly positive. Firms should align CSR with resource capabilities, emphasizing capital efficiency and human capital to mitigate costs. Policymakers should support firms beyond disclosure mandates. This study provides longitudinal evidence from an emerging market, showing that CSR value is conditional on resource efficiency rather than inherently beneficial. Full article

The accurate prediction of hydrodynamic characteristics and structural responses in underwater fishing gear is critical for optimizing design, ensuring operational safety, and minimizing environmental impact. To overcome the computational costs and scalability limitations of traditional physical modeling, this study evaluates three machine learning algorithms such as Random Forest (RF), Light Gradient Boosting Machine (LightGBM), and Support Vector Machine with a Radial Basis Function kernel (SVM-RBF) to predict the hydrodynamic coefficients and structural responses of tuna longline components, including mainlines and branch lines. Models were trained and validated using a comprehensive flume tank dataset encompassing six gear configurations tested across varying flow velocities and lead-line weights. Results demonstrate that optimal model selection is inherently task dependent. For hydrodynamic coefficients, LightGBM achieved superior predictive accuracy for branch-line drag (whole-dataset R² = 0.8315), while both LightGBM and SVM-RBF excelled in lift prediction. Conversely, structural responses (sinking depth and x-displacement) proved inherently more difficult to model deterministically due to high-frequency transient dynamics and stochastic variability. While LightGBM provided balanced generalization for sinking depth, SVM-RBF exhibited severe overfitting for x-displacement. In contrast, RF maintained the most conservative and consistent performance across structural targets, effectively mitigating the memorization of dynamic noise observed in the more complex algorithms. Beyond predictive modeling, feature importance analysis identified flow velocity, lead-line weight, material stiffness, and geometric parameters as dominant physical drivers, validating the physical plausibility of the models. Crucially, the integration of experimental and ML analyses revealed that a polylactic acid (PLA)-integrated midsection configuration consistently yielded the lowest and most stable drag force (0.004–0.13 N at 0.49 m/s), representing a 30–60% reduction compared to conventional nylon lines. Furthermore, the study uncovered novel physical phenomena, including velocity-independent deformation stability, progressive transient sinking kinetics, and tension-induced load redistribution. These findings establish machine learning as a reliable, scalable surrogate for longline gear design, advocating for thin-diameter, biodegradable PLA-integrated lines to enhance hydrodynamic efficiency and mitigate marine plastic pollution, while underscoring the necessity of task-specific algorithm selection for robust engineering applications. Full article

The instability of marine low-sulfur fuel oil caused by asphaltene precipitation poses significant operational challenges in the shipping industry. This study systematically investigates the dispersing effects of biodiesel derived from three different feedstocks—palm oil, waste cooking oil (WCO), and microalgae oil—and their individual fatty acid methyl ester components on asphaltenes extracted from VLSFO. Biodiesel was selected as a dispersant due to its renewable nature, polar ester functional groups, and variable unsaturation levels, which enable favorable interactions with asphaltene molecules through hydrogen bonding and π-π stacking. Using UV–Visible spectrophotometry, the dispersion performance was quantitatively evaluated under various conditions including dispersant concentration, temperature, storage time, and molecular structural characteristics. The results demonstrate that microalgae oil biodiesel exhibits the most superior asphaltene dispersion capability among the three biodiesels, with a dispersion improvement index of 35% at 12 g/L, compared to 28% and 22% for waste cooking and palm oil biodiesels. Optimal performance is achieved at 80 °C, where the asphaltene concentration increases by 68% relative to the control, and remains stable within the first 10 days of storage but deteriorates significantly after 30 days due to oxidative degradation. Among individual FAME components, the dispersion effectiveness increases with alkyl chain length from C10 to C20, with the latter reaching a 30% improvement index. Functional group polarity plays a critical role, with carboxylic acid exhibiting a 45% improvement at 14 g/L, substantially outperforming alcohol at 32% and ester at 28%. The degree of unsaturation further enhances dispersion, as the improvement index rises progressively from 20% for saturated methyl stearate to 42% for tri-unsaturated methyl linolenate, representing a 2.1-fold increase. Dynamic light scattering (DLS) measurements confirm that biodiesel addition reduces asphaltene particle size from the micrometer range of 2 to 5 μm down to submicron levels of 200 to 500 nm, while microscopic observations reveal inhibited aggregation. These findings provide theoretical foundations for biodiesel application in marine fuel systems. Full article

Terahertz time-domain spectroscopy (THz-TDS) is a widely used technique for characterizing a broad range of materials in the terahertz frequency region. Conventional THz-TDS requires both reference and sample signals to extract optical parameters such as refractive index and absorption coefficient. Determining optical anisotropy, specifically birefringence and linear dichroism, typically requires separate measurements of the optical parameters of the sample parallel and perpendicular to the terahertz electric field. This process increases measurement time and depends heavily on a stable reference scan. In this work, we present a simple and accurate method to directly obtain birefringence and linear dichroism without the need for a reference measurement. The proposed approach extracts anisotropic parameters solely from the sample signals by analyzing the differential phase delay and amplitude attenuation between orthogonally polarized terahertz electric field components. We validate this method experimentally using optically anisotropic materials such as TiO₂ and bamboo samples and confirm that the results agree closely with those from conventional reference-based THz-TDS. This technique offers a practical route to measure the optical anisotropy of materials, particularly in situations where acquiring a reference signal is challenging. Full article

Real-world IoT network security generates traffic at big-data scale with extreme class imbalance, temporal non-stationarity, and continuously evolving attack strategies that overwhelm static supervised classifiers. This paper presents a cognitive computing framework for network intrusion detection: a CNN–LSTM–DQN architecture with Prioritized Experience Replay (PER) evaluated on a 5,000,000-flow naturalistic sample of the TON_IoT Processed_Network dataset (4,000,000 training/1,000,000 temporally held-out test flows; 94.5% attack ratio) under a strict temporal split. The cognitive agent optimizes detection decisions using an Alerts per Million Flows (ARMF)-aware reward function that encodes both alert-fatigue cost and missed-attack penalty. We conduct a cross-attack-family generalization study: the methodology—architecture template, reward design, and hyperparameter calibration—is inherited from a framework previously validated on CSE-CIC-IDS2018, re-instantiated and retrained on the structurally different TON_IoT environment, and compared against the previously published benchmark. Initialization sensitivity is characterized across five independent random seeds using paired Wilcoxon signed-rank and t-tests. Across the five seeds, the proposed X2 model attains recall 0.833 ± 0.306 and F1 0.874 ± 0.241 (mean ± sample SD), versus the supervised X1 baseline at 0.858 ± 0.178 and 0.912 ± 0.116; the best-performing seed (42) achieves 97.52% accuracy, 98.02% attack recall, 99.46% precision, and 98.73% F1-score on 1,000,000 held-out XSS flows—an attack family entirely absent from training—with temporal stability variances of 4.63 × 10⁻⁷ (recall) and 1.38 × 10⁻⁷ (F1). The X2 advantage observed among the four stable seeds is not statistically demonstrated at n = 5 (statistical power ≈ 5.1%); the initialization-sensitivity finding itself, including one degenerate alert-suppression seed, is reported as a primary contribution. A formal, exactly additive ARMF decomposition distinguishes the detected-attack (structural) component (99.46%) from the model-induced false-positive component (0.54%), and we report a multi-seed, ARMF-aware cognitive IDS evaluation on naturalistic TON_IoT traffic under an unseen-attack-family test condition that, to the best of our knowledge, has not been reported in the surveyed RL-based NIDS literature. Full article

Background: Skin photoaging, primarily induced by chronic ultraviolet (UV) radiation exposure, is characterized by dryness, wrinkle formation, pigmentation abnormalities, and reduced skin elasticity, resulting from oxidative stress, inflammation, and degradation of the extracellular matrix. Codonopsis pilosula, a traditional food–medicine homologous plant, is recognized for its anti-aging properties. However, its lipophilic components (designated as CP-L) remain insufficiently explored. Methods: Herein, we developed a thermosensitive nanogel encapsulating CP-L-loaded transferosomes (CP-L nanogel) to enhance topical delivery and evaluated its effects in both a UV-induced photoaging mouse model and UVB-irradiated HaCaT keratinocytes. Results: In UV-induced mice, topical application of the nanogel markedly reduced skin wrinkling and epidermal hyperplasia, with epidermal thickness decreased by 83.2% compared to the model group (p < 0.01), and restored skin elasticity and collagen deposition, as evidenced by a 35.5% increase in collagen area fraction (p < 0.01). Correspondingly, in UVB-irradiated HaCaT cells, it significantly increased cell viability from 53.0 ± 9.6% to 89.4 ± 1.0% (p < 0.01) and suppressed apoptosis from 30.1 ± 0.48% to 12.4 ± 0.66% (p < 0.01). Furthermore, the CP-L nanogel consistently attenuated oxidative stress, with SOD, CAT, and GSH-Px activities increased by 73.1%, 188.1%, and 18.2%, respectively (p < 0.01), and MDA levels reduced by 71.0% (p < 0.01), while inflammatory responses were suppressed, as TNF-α, IL-1α, IL-1β, and IL-6 levels decreased by 28.3%, 22.6%, 12.8% and 31.9%, respectively (p < 0.01). Mechanistically, transcriptomic and molecular analyses revealed that the nanogel potently inhibited the UV-induced activation of the FGFR/PI3K/AKT/mTOR/p70S6K signaling cascade at both transcriptional and protein levels, with the phosphorylation levels of FGFR, PI3K, AKT, mTOR, and p70S6K significantly reduced by 43.9%, 30.9%, 38.8%, 34.9%, and 57.3%, respectively (p < 0.01). Molecular docking and dynamics simulations identified isofuranodienone and aromadendrene oxide-(2) as key constituents with high-affinity, stable binding to FGFR1 and AKT1. The cytoprotective effect of the nanogel was completely abolished by co-treatment with the FGFR inhibitor PD173074, confirming functional reliance on this pathway. Enhanced cellular delivery of the formulation was directly demonstrated by flow cytometry, showing an approximately 1.8-fold increase in cellular uptake compared to the free drug (p < 0.01). Conclusions: Collectively, these results demonstrated that the CP-L nanogel alleviated skin photoaging through a multi-faceted mechanism involving enhanced cellular delivery, potent antioxidant and anti-inflammatory activities, and specific inhibition of the FGFR/PI3K/AKT/mTOR signaling cascade, highlighting its potential as a multitargeted topical agent derived from an edible plant. Full article

Background: Uveal melanoma (UM) represents the most prevalent primary intraocular malignancy in adults, yet patients harboring GNAQ/GNA11 mutations face particularly poor prognoses with median survival of merely 6–12 months following metastasis. Multi-targeted combination therapy offers a promising strategy to circumvent drug resistance. The present study investigated the synergistic anti-tumor efficacy and mechanistic basis of Isoliquiritigenin (ISL), Quercetin (Que) and Methylnissolin (Meth), three bioactive constituents from Astragalus membranaceus (Huangqi, a widely used traditional Chinese medicinal herb) against UM. Methods: Molecular docking and 100 ns molecular dynamics simulations assessed binding stability between the compounds and their respective targets (Gαq, MEK and YAP). Synergistic interactions were quantified using the Zero Interaction Potency (ZIP) model, a reference synergy model that compares observed combination effects to predicted non-interaction baselines across full dose–response matrices, based on CCK-8 assays. Cell cycle distribution, apoptosis and mitochondrial membrane potential were analyzed by flow cytometry. Western blotting detected target proteins and apoptotic markers. A male BALB/c nude mouse xenograft model validated therapeutic efficacy and systemic safety. Results: Molecular docking revealed binding energies <−7.0 kcal·mol⁻¹ for all three drug–target pairs, with molecular dynamics trajectories confirming stable complex conformations (RMSD < 3 Å). In vitro, the ISL-Que-Meth (IQM) combination exhibited strong synergism (ZIP scores > 10), significantly increasing apoptotic rates, collapsing mitochondrial membrane potential and upregulating cleaved-caspase 9 expression compared with monotherapy, and a modest G2/M phase accumulation was also observed, although the magnitude was limited relative to apoptotic induction. In vivo, the triple combination achieved approximately 50% reduction in tumor growth compared with the control group, with effects comparable to or exceeding those of the clinical reference agent Trametinib, and reduced Ki67 proliferation indices while elevating cleaved-caspase 9 levels, without eliciting hepatorenal toxicity. While these data demonstrate therapeutic efficacy, they do not establish in vivo synergy, as single-agent and dual-combination arms were not included in the xenograft design. Conclusions: These findings demonstrate that IQM synergistically suppresses UM growth in association with coordinated modulation of Gαq/MEK/YAP axis components and caspase 9-dependent apoptosis via the intrinsic mitochondrial pathway, providing preclinical evidence for natural product-based multi-targeted therapy against UM. Full article

Sex estimation is a fundamental component of biological profiling in forensic anthropology, particularly when skeletal remains are incomplete or fragmented. This study aimed to evaluate sex estimation of the cranial base using geometric morphometrics and to assess the predictive value of cranial base morphology for sex estimation. The study included 211 adult skulls (139 male, 72 female) from the Bosnian population. Each skull was digitized to generate 3D models, and 27 anatomical landmarks were recorded. Landmark coordinates were standardized using Generalized Procrustes Analysis, Principal Component Analysis, Discriminant Function Analysis with permutation testing, and regression of shape on centroid size. Statistically significant sex estimation was observed at both the form (shape and size) and shape levels. Classification accuracy based on cranial base form reached 92.81% for males and 86.11% for females. Shape-based classification, after removal of size effects, also showed high accuracy (90.65% for males and 81.94% for females). Regression analysis indicated that size contributed significantly but modestly to shape variation. The cranial base exhibits stable sexually dimorphic patterns and may represent a reliable anatomical region for sex estimation. These findings contribute to population-specific standards for the Bosnia and Herzegovina population and support the forensic applicability of 3D geometric morphometric approaches. Full article

This paper proposes a federated learning framework for privacy-preserving patient monitoring with lightweight blockchain anchoring. The framework keeps synthetic patient monitoring records local at each client and uses federated model aggregation to support collaborative learning without centralizing raw records. To improve traceability, the blockchain layer is specified as an anchoring mechanism that records compact evidence, including model hashes and participation metadata, rather than raw data or full model parameters. Experiments were conducted on synthetic patient monitoring data to evaluate framework behavior under non-IID client distributions, label noise, different client counts, partial client participation, and aggregation strategies. The centralized MLP baseline achieved approximately 0.89 overall accuracy but failed to detect alert cases, with 0% alert-class recall, showing that accuracy alone can be misleading in imbalanced monitoring scenarios. In the federated simulations, the model reached approximately 0.99 accuracy under clean labels, approximately 0.90 under 10% label noise, and approximately 0.70 under 30% label noise. Under a more difficult noisy, non-IID, dropout, and fixed skewed-client evaluation setting, the model stabilized at approximately 0.80 accuracy after 25 communication rounds. Client scaling from 5 to 20 clients remained stable, and FedAvg, weighted aggregation, and accuracy-trimmed robust aggregation produced similar final accuracy of approximately 0.98 in the 10-client setting. The results indicate that label quality strongly affects federated convergence, while blockchain anchoring should be interpreted as an auditability mechanism rather than a direct accuracy-improving component. This study provides a framework-level foundation for auditable federated patient monitoring in semi-trusted healthcare networks. Full article

Accurate identification of electromagnetically induced stator vibration frequency components is essential for the online condition monitoring of hydro-generators, particularly for assessing the dynamic state of the stator core under normal operating conditions. In engineering practice, the fast Fourier transform (FFT) is widely used for vibration spectrum analysis; however, because the measured vibration response is simultaneously affected by electromagnetic excitation, mechanical rotation, hydraulic disturbance, and external harmonic interference, FFT-based spectra often contain multiple frequency components whose structural relevance is difficult to determine directly. To address this issue, this paper proposes a coupled continuous wavelet transform and power spectral density transmissibility (CWT-PSDT) method for identifying key vibration frequency components with stable time-frequency energy and inter-sensor transmissibility in hydro-generator stator vibration signals. In the proposed framework, the analytic Morlet wavelet is first employed to localize dominant energy bands in the time-frequency domain, and PSDT is then used to screen frequency components with relatively stable inter-sensor transmissibility characteristics, thereby reducing the ambiguity caused by excitation-dominated spectral components. A clamped-clamped beam model is first used for numerical validation, and the maximum identification error of the first five natural frequencies is 4.22%. Experiments on a Francis turbine-generator test rig under five operating conditions further show that the proposed method can distinguish the mechanical rotational component near 10.3 Hz from the electromagnetic-related component near 50.8 Hz, while retaining higher-order electromagnetic-related components around 150 Hz and 250 Hz. The results demonstrate that the proposed CWT-PSDT method provides a physically interpretable and data-efficient approach for extracting stator-core-related spectral features, and offers a theoretical basis for spectrum-based online monitoring and future abnormal-condition comparison of hydro-generator stator responses. Full article

Hydrogen isotope retention in tungsten plasma-facing components affects particle recycling, plasma-density control, and tritium inventory management in fusion devices. In this study, a glow discharge cleaning (GDC) platform and a thermal desorption spectroscopy (TDS) system were used to investigate deuterium (D) retention in polished tungsten and the response of the retained D signal to He-GDC, H₂-GDC, and thermal baking treatments. Tungsten samples were first loaded by D₂-GDC and then characterized by TDS during programmed heating from room temperature to 917 °C at 10 K min⁻¹. Under the present data-reduction procedure, the D₂-GDC-only sample gave an apparent TDS-equivalent D inventory of approximately 7.85 × 10¹⁹ D m⁻², corresponding to about 29.5% of the estimated incident D fluence. Relative to this D₂-GDC reference sample, the normalized removal fractions were 9.3% for low-power He-GDC, 28.2% for high-power He-GDC, and 65.6% for the tested H₂-GDC condition. The H₂-GDC result is consistent with an isotope-exchange-assisted removal pathway, but the gas-species effect cannot be isolated because the H₂ and He treatments were not performed with identical pressure, duration, current, voltage, and sample-temperature histories. Stepwise baking at 240–350 °C did not fully suppress subsequent D-derived TDS release, indicating that part of the retained D remained in more stable trapping states. Because independent replicates and certified HD/D₂ sensitivity calibration were not available for all conditions, the inventories are reported as apparent values and the conclusions are restricted to normalized comparisons under the tested laboratory conditions. Full article

Background/Objectives: Contemporary endometrial cancer pathology integrates histological and molecular features for risk classification. Endometrioid endometrial carcinoma (EEC) with a squamous cell carcinoma (SCC) component was historically termed adenosquamous carcinoma but later incorporated into the squamous-differentiation subtype of EEC. This reclassification left an evidence gap, and its prognostic significance remains insufficiently defined. We compared clinicopathological aggressiveness and evaluated associations of an SCC component with overall survival (OS) and lymph node (LN) involvement. Methods: We retrospectively analyzed 7590 surgically staged patients from 24 institutions (2000–2019): 7495 pure EEC and 95 EEC with SCC components. Confounding was addressed using overlap weighting (OW), inverse probability of treatment weighting targeting the average treatment effect on the treated (IPTW-ATT), and 1:4 propensity score matching (PSM), each with doubly robust Cox regression. Survival machine-learning models with SHapley Additive exPlanations (SHAP) assessed histological subtype contribution. LN involvement was assessed by OW/IPTW-ATT-weighted logistic regression. Results: EEC with SCC component had higher grade, deeper myometrial invasion, more advanced stage, and worse unadjusted OS (hazard ratio [HR] 7.04; 95% confidence interval [CI] 3.50–14.16; p < 0.001). After adjustment, the OS disadvantage persisted: OW-adjusted HR 3.05 (95% CI, 1.36–6.85; p = 0.007), IPTW-ATT-adjusted HR 3.26 (95% CI, 1.51–7.01; p = 0.003), and PSM-adjusted HR 3.30 (95% CI, 1.24–8.77; p = 0.017). XGBoost-Survival showed stable discrimination (test C-index 0.797 ± 0.057; 5-year AUC 0.787 ± 0.065), and SHAP supported histological subtype as a prominent risk-associated contributor after class-imbalance correction. In contrast, adjusted LN involvement was not increased (OW odds ratio [OR] 1.000, 95% CI 0.505–1.979; p = 1.000; IPTW-ATT OR 1.035, 95% CI 0.527–2.035; p = 0.920). Conclusions: EEC with SCC component was associated with worse OS after adjustment, without an adjusted increase in LN involvement. These findings support explicit morphologic reporting and future validation incorporating systematically recorded molecular testing when available. Full article