Search Results (10,442)

Background/Objectives: Uterine adenosarcoma is a rare uterine malignancy with limited evidence on prognostic factors and long-term outcomes. We aimed to evaluate clinicopathological characteristics, treatment patterns, oncologic outcomes, and prognostic factors associated with disease-free survival (DFS) and overall survival (OS) in patients with uterine adenosarcoma. Methods: This multicenter retrospective cohort study included 43 patients with histopathologically confirmed uterine adenosarcoma who underwent primary surgery at seven tertiary referral centers in Türkiye between 2016 and 2026. Clinicopathological features, treatment modalities, recurrence patterns, and survival outcomes were assessed. Survival was analyzed using Kaplan–Meier and log-rank methods, and multivariable Cox proportional hazards regression was performed to identify independent prognostic factors. Results: The mean age at diagnosis was 59.1 ± 10.8 years, and 83.7% of patients were postmenopausal. Pelvic pain (62.8%) and abnormal uterine bleeding (60.5%) were the most common presenting symptoms. Most patients (79.1%) underwent laparotomy, and 79.1% had stage I disease. Sarcomatous overgrowth (SO) was present in 44.1% and lymphovascular space invasion (LVSI) in 18.6%. During a median follow-up of 72 months, 34.9% experienced recurrence, most commonly in the pelvis. The five-year DFS and OS rates were 59.9% and 80.4%, respectively. SO was independently associated with DFS (HR 4.41, 95% CI 1.21–16.08; p = 0.025) and OS (HR 10.23, 95% CI 1.16–89.80; p = 0.036), while LVSI was independently associated with OS (HR 11.17, 95% CI 1.34–93.14; p = 0.026). Conclusions: Uterine adenosarcoma showed favorable long-term survival but substantial recurrence risk. SO and LVSI may have potential prognostic relevance and could contribute to postoperative risk assessment and individualized follow-up. Full article

With the rapid advancement of space-based laser weapon technologies, on-orbit safety of spacecraft such as satellites is confronted with severe laser threats. To meet the demands for film system optimization and reliability improvement of thin films integrating space laser protection and thermal control functions, this study takes the Graphene/Ag/Al₂O₃/SiO₂/ITO multilayer thin film structure as the research object. Combined with the space alternating high-low temperature environment and the action of ultra-high-density transient directional heat flux, systematic simulation research on the evolution laws of temperature and stress fields inside the multilayer thin films under laser irradiation in alternating space high-low temperature environments is carried out via COMSOL Multiphysics, and the influencing mechanisms of ambient temperature and laser operating parameters on thermal stress and temperature distribution are revealed. The simulation results demonstrate that the thin film structure reaches thermal equilibrium within several seconds under a given transient directional heat flux. As laser power rises, the peak temperature of each layer increases nonlinearly and the time required to reach thermal equilibrium shortens. The laser heat flux density acts as the dominant factor governing the temperature and thermal stress distribution. Under alternating space high-low temperature conditions, the thermal stress of the thin film varies approximately linearly with temperature while the overall stress magnitude remains low, and thermal stress is mainly concentrated in the Al₂O₃ layers. Laser loading exerts a remarkable impact on film thermal stress: the amplitude of thermal stress in all film layers rises synchronously with increasing laser power, and interlayer temperature gradients as well as stress concentration are further intensified. The stress growth of Ag and Al₂O₃ layers is the most significant, which can be attributed to the synergistic effect of interlayer thermal expansion coefficient mismatch and temperature gradients. The alternating high-low temperature and laser irradiation experiments indicate that the maximum temperature and maximum stress borne by the muti-layer film under alternating temperatures ranging from −150 °C to 150 °C and laser irradiation of 200 W/cm² will not lead to macroscopic failure behaviors and degradation of thermal control performance. Full article

Background: Adequate patient self-care is fundamental to effective heart failure (HF) management and is endorsed by international guidelines to improve clinical outcomes. Despite a growing body of evidence, the global research landscape, including publication trends, collaborative networks, and research hotspots, remains poorly characterized. Methods: Publications on HF self-care were extracted from the Web of Science Core Collection from database inception to 29 June 2026, restricted to the categories of “Nursing” or “Cardiac & Cardiovascular Systems” and to English-language articles and reviews. Bibliometric analyses were performed using VOSviewer and CiteSpace for specific characteristics such as publication year, countries, institutions, authors, journals, keywords, and co-cited references. Results: A total of 2867 publications were included, revealing a sustained increase in annual output and citations, with a marked acceleration since 2008. The United States dominated global production (1362 publications, 47.51%) and academic impact. The University of Pennsylvania was the most prolific institution, and Riegel B was the leading author. Keyword and co-citation analyses identified four interconnected thematic domains: barriers to self-care and their prognostic impact, psychosocial factors and patient-reported outcomes, intervention strategies and healthcare delivery, and caregiver burden and dyadic interaction. Digital health, particularly telemedicine and mobile health, has emerged as a rapidly evolving frontier. Conclusions: HF self-care research continues to grow rapidly, with future efforts focused on caregiver burden and dyadic dynamics, equitable digital health, and personalized management for older adults with multimorbidity and cognitive impairment to ultimately improve clinical outcomes and quality of life for patients with HF. Full article

Against the backdrop of China’s Dual Carbon Initiative and national transportation empowerment strategy, accelerating the low-carbon green transition of the transport sector has emerged as an imperative developmental priority. Incorporating carbon emissions as undesirable outputs into the efficiency evaluation framework, this study adopts a multi-method analytical paradigm encompassing the super-efficiency SBM model, Malmquist–Luenberger index, kernel density estimation, Dagum Gini coefficient, geographical detector model, and Geographically and Temporally Weighted Regression (GTWR). Based on panel data covering 30 provincial administrative regions in China from 2004 to 2022, this paper systematically investigates the spatio-temporal evolutionary patterns and intrinsic driving mechanisms of carbon total factor productivity (CTFP) within the transportation industry. The main findings are as follows: (1) The static efficiency results reveal that the national mean CTFP is below unity, indicating overall inefficiency. Nevertheless, it exhibits a fluctuating upward trend after 2009. Regionally, CTFP follows this pattern: Eastern China > Central China > national mean > Northeastern China ≈ Western China. (2) Dynamic productivity analysis shows that the annual average ML index is close to 1, demonstrating an overall upward trend in CTFP, and productivity growth is primarily driven by technological progress. (3) In terms of spatio-temporal patterns, inter-regional disparities constitute the principal source of overall spatial gaps in CTFP, with considerable contribution from transvariation density. (4) Geographical detector analysis suggests that energy intensity (EI) and economic development level are the two factors most strongly correlated with the spatial differentiation of CTFP, and interaction effects exist between them. The results from geographically weighted regression (GWR) further confirm that the strength of the correlation between each driving factor and CTFP presents pronounced regional heterogeneity. Based on these findings, differentiated regional policies for emission reduction and efficiency improvement should be formulated in accordance with the law of diminishing marginal returns. Full article

The Danjiangkou Reservoir Area is a critical water source for the Middle Route of the South-to-North Water Diversion Project, making its ecological security of regional importance. Using land-use data from 1995, 2005, 2015, and 2023, this study classified the area into production, living, and ecological spaces and assessed the spatiotemporal evolution of landscape ecological risk. Spatial autocorrelation analysis and the optimal parameters-based geographical detector were further applied to identify spatial clustering and driving factors. Ecological space remained dominant throughout the study period, accounting for more than 77% of the total area, whereas production space declined and living space expanded. Landscape ecological risk was predominantly moderate to high, with high-risk areas concentrated around the reservoir, along the Hanjiang and Danjiang rivers, and in zones of intensive human activity, while low-risk areas were mainly located in peripheral mountainous and forest-dominated regions. Risk exhibited significant positive spatial autocorrelation, with Moran’s I decreasing slightly from 0.585 to 0.573. Population density had the strongest individual explanatory power, whereas the interaction between elevation and population density showed the highest explanatory power (q = 0.880–0.888). These findings support spatially differentiated ecological protection and territorial optimization in the reservoir area. Full article

Historical pax demand estimation is important for understanding long-term demand dynamics and supporting airline planning, strategic decision-making, and transportation policy analysis. However, current decomposition approaches either estimate trends without explicitly modeling seasonality or jointly estimate trend and seasonal components without providing user control over trend smoothness. This paper proposes a Guerrero–Holt–Winters (GHW) hybrid smoothing model to estimate and reconstruct historical route-level airline pax demand. The GHW model combines Guerrero’s user-controlled trend estimation with the multiplicative seasonal structure of the Holt–Winters (HW) model to produce an interpretable decomposition of historical demand into trend, seasonal, and irregular components. The model is applied to quarterly pax demand data for ten representative U.S. domestic air routes covering the period 2015–2023. The results show that the proposed GHW model achieves lower in-sample reconstruction errors than the multiplicative HW model at lower degrees of trend smoothness within the evaluated range. Among the evaluated specifications, λ = 1, corresponding to a Guerrero smoothness index of S(1; 36) = 58.82%, produces the lowest in-sample reconstruction error. The historical reconstruction error, measured by the root mean squared error (RMSE), is reduced by approximately 54.4% relative to the multiplicative HW model. Similar improvements are observed under MAE, MAPE, and sMAPE, indicating that the reduction in reconstruction error at this smoothness level is consistent across the four error measures. An expanded benchmark comparison with HW, ETS state-space exponential smoothing, STL decomposition, and HP1600 further shows that the GHW model produces the lowest in-sample reconstruction errors across all four evaluation error metrics and all ten routes analyzed. A sensitivity analysis further demonstrates that reconstruction accuracy declines as the degree of trend smoothness increases from S(λ; 36) = 58.82% to 88.63%, whereas the estimated seasonal factors remain remarkably stable across the evaluated smoothness range. A COVID-19 robustness analysis further shows that reconstruction errors are substantially lower in the Pre-COVID period than in the Post-COVID period across all ten routes, indicating that reconstruction accuracy is sensitive to the period analyzed. Full article

The Liao Dynasty culture among the northern nomadic peoples absorbed profound West Asian historical heritage and uniquely ingenious artistic essence to enrich its own diversity. These mural paintings of Liao tombs most concentratedly reflect this phenomenon. As a visual medium for cultural integration along the Steppe Silk Roads, their artistic expression profoundly reflects the Liao Dynasty’s process of absorbing West Asian art. This article examines the tomb of Zhang Shiqing 张世卿, a former Song subject, now an official of the Liao Dynasty. It employs image analysis alongside interdisciplinary theories from art history, geography, archaeology, and history, aiming to reveal the logic of localized adaptation of West Asian artistic elements—the zodiac signs (Greek: zōidiakos kyklos, “circle of animals”; Chinese: 黄道十二宫图) and the curved-neck pipa—within the murals. The article argues that multiple underlying factors within the Liao Dynasty’s political-cultural structure, Buddhist propagation, and functional demands drove both the zodiac signs and the curved-neck pipa to exhibit a pattern of knowledge-integration adaptation. This adaptation manifests as juxtaposition and fusion in visual composition, functional integration and recontextualization in spatial arrangement, achieving a creative transformation of West Asian artistic motifs. This process not only revitalized the ritual space of the murals but also represented the inevitable projection of dualistic systems within the cultural sphere of the Liao Dynasty. Full article

Environmental, social, and governance (ESG) quality cannot be directly observed because substantial disagreement across rating providers contaminates the observed ESG scores with measurement error. This paper addresses this problem by modelling the true ESG state as a latent, vector-valued Itô diffusion defined on a filtered probability space whose information set is progressively enlarged by FinTech signals. We establish the well-posedness of the latent ESG process, prove the existence of an equivalent martingale measure under an explicit exponential-moment condition, and solve an ESG-constrained portfolio problem under a wealth-scaled sustainability constraint through a Hamilton–Jacobi–Bellman verification theorem. Computationally, raw sustainability information is extracted from SEC Form 10-K filings using a FinBERT transformer architecture and incorporated into a linear Gaussian state-space model, where the latent ESG state is recovered via Kalman filtering. Theoretical results are then linked to asset pricing, portfolio allocation, and systemic risk networks through a common filtered ESG factor. Using an unbalanced panel of 1086 U.S. listed firms over 2011–2023 and ESG information from MSCI, Refinitiv, and Sustainalytics, we document substantial provider disagreement and show that the observed ESG ratings contain significant transitory measurement noise. The filtered ESG state exhibits higher reliability, lower noise, and greater persistence than individual provider scores. In asset pricing tests, the latent ESG state predicts future excess returns, whereas a composite provider-based ESG measure does not; a one-standard-deviation increase in the latent ESG state is associated with approximately 0.35 percentage points higher monthly excess returns (about 4.3% annualised). When both measures are included simultaneously, only the filtered ESG state retains explanatory power. Out-of-sample portfolio tests show that a latent ESG strategy achieves a Sharpe ratio of 0.72, significantly exceeding both an unconstrained benchmark (0.59) and a composite ESG screen strategy (0.55). At the network level, ESG-adjusted weighting attenuates systemic fragility by reducing the spectral abscissa from 0.34 to 0.21, with the mitigating effect remaining significant under permutation-based placebo tests. Overall, the evidence supports the central hypothesis that ESG measurement error attenuates the observed pricing effects and that FinTech-enabled filtering recovers economically meaningful sustainability information relevant for asset pricing, portfolio construction, and systemic risk assessment. Full article

Underwater images captured by sensors suffer from low contrast and blurry details due to the interference of light absorption and scattering in underwater scenes. Good visibility restoration is often desired for practical processing applications. Current image enhancement methods often rely on prior assumptions to reconstruct a clear image without considering the inherent correlation of underwater image degradation, introducing unconsiderable enhancement results. Thus, this paper proposes an underwater enhancement method based on multiple enhanced layers fusion and transmission-driven color restoration, named EFCR, which consists of three key modules: a pixel-based transmission computation (PTC), a multiple-enhanced-layers fusion (MELF), and a transmission-driven color restoration (TCR). First, PTC designs a linear transformation to adjust the saturation and estimates the transmission based on the mapping relationship between the transmission, the brightness, and the saturation, preventing the transmission from being under-estimated. Then, MELF extracts the original details from the luminance channel and enhances these desired details based on the estimated transmission. Meanwhile, adaptive histogram equalization is used to improve the global brightness. Finally, TCR further analyzes the inherent correlation between the transmission and the image degradation, and constructs a compensation factor to adaptively correct the attenuated a and b channels of Lab space, producing a good enhancement result with reasonable brightness and natural colors. Extensive experiments on three underwater image datasets demonstrate the effectiveness and robustness of the proposed method in underwater image restoration. Especially, the average $\overline{r}$ and Blur values of our method at most incline and decline by 99.87% and 10.22%, respectively, which shows our method has obvious advantages in edge enhancement and haze removal. Moreover, our method provides helpful support for color restoration and image salient detection, and also shows good generalization capability for enhancing outdoor hazy images. Full article

This study examines how the vertical spacing of a multi-height 360° camera array affects novel-view synthesis quality in indoor scenes reconstructed with 3D Gaussian splatting (3DGS). Although 3DGS enables high-quality real-time scene representation, its output depends on the geometric arrangement of the input views, which single-lens workflows secure through repeated captures at several heights. A rig of three vertically arranged 360° cameras was evaluated in four indoor spaces differing in ceiling height, structure, and capture path. Three spacing conditions were applied in each space, denoted as Set-L, Set-D, and Set-H. These are ordinal positions within the vertical range each space allows rather than fixed absolute distances, since an identical spacing sits differently in rooms of different scale. The 360° footage was stitched into equirectangular video and reframed into multi-view image sequences, yielding 120 datasets from four spaces × three conditions × ten repeated captures. Novel-view synthesis quality was measured with the PSNR, SSIM, and LPIPS on validation views withheld from training. Because normality and homogeneity of variance were not satisfied, a robust two-way factorial analysis of variance based on 20% trimmed means was used, with robust post hoc comparisons. Spacing, spatial characteristics, and their interaction were significant for all three metrics, so no single spacing was preferable across every space. The wide setting performed best in the low-ceiling repetitive space, the narrow setting in the open space, and the baseline setting in the largest space with variable ceiling height. In one space, comprising stepped, near-symmetric seating, no condition was distinguishable, and dispersion across repeated captures was an order of magnitude larger than elsewhere, indicating that, where a repetitive structure is extensive, the limiting factor is capture stability rather than the choice of spacing. The findings are consistent with a trade-off between vertical viewpoint separation and inter-view overlap, and provide exploratory, space-conditional reference points for indoor 3DGS capture rather than a standardized specification, with relevance to virtual exhibitions, architectural visualization, and digital-twin construction. Full article

The increasing demand for electrified rail transportation has intensified power quality (PQ) challenges, including harmonics, voltage imbalance, and low power factor (PF). These issues have driven the development of advanced traction power supply systems, particularly co-phase configurations, to improve power quality, enhance grid-connected stability, and strengthen the operational resilience of railway power infrastructure. This paper proposes a co-phase power supply system for high-speed railways that facilitates high-speed train operation by integrating power quality compensation technologies while reducing the required number of neutral sections by half, thereby improving the continuity and robustness of traction power delivery. To address PQ issues, a capacitive-coupled hybrid power quality conditioner (HPQC) incorporating renewable energy sources (RESs) into its DC link is introduced. Given the highly dynamic and time-varying nature of railway loads, a sliding mode control (SMC)-based robust control method is developed based on the state space model of the co-phase power supply system and the HPQC to provide a stable and rapid response to load variations and operational disturbances. The effectiveness and real-time implementation capability of the proposed approach are validated through real-time control hardware-in-the-loop (CHIL) simulations. Results from MATLAB/Simulink simulations and real-time CHIL testing demonstrate substantial harmonic reduction, improved power factor, reduced negative-sequence currents, and enhanced overall system efficiency. These outcomes confirm the suitability of the proposed system for modern high-speed railway applications and highlight its contribution to resilient traction power supply systems capable of maintaining reliable operation under highly variable loading conditions. Full article

This paper proposes an inverter constraint-based EL map screening method for selecting pole–slot combinations of an axial flux permanent magnet motor (AFPM) for collaborative robot joints according to the requirements of the actual drive system. First, an initial set of candidates is identified based on the winding factor and pole and slot characteristics. The inductance and no-load back-EMF of each candidate are then represented on a common EL map, enabling different pole and slot topologies to be compared within the same electromagnetic parameter space. Subsequently, the required current and voltage for generating the target torque are calculated and evaluated against the inverter voltage and current limits to assess the system-level drive suitability of each candidate. Among the candidates satisfying both the voltage and current constraints, the current margin is used as the primary selection criterion, while the harmonic characteristics of the no-load back-EMF are additionally considered. Based on this evaluation, the 22-pole and 24-slot combination is selected for detailed design. For the selected AFPM, a detailed 3D finite element method (FEM) design is performed considering the number of turns, permanent magnet length, magnet spacing, and tooth spacing as design variables. Under the same motor volume constraint as the conventional radial flux permanent magnet motor (RFPM), the final AFPM increases the load torque from 0.737 N·m to 0.907 N·m and the output power from 263.6 W to 332.4 W, corresponding to improvements of 23.1% in torque density and 26.1% in power density, respectively. Full article

Soil salinization constrains cotton production in arid regions, making the optimization of water and salt regulation in drip irrigation systems crucial for enhancing water use efficiency and yield in saline cotton fields. This study aimed to evaluate the synergistic effects of emitter parameters and irrigation quotas on water–salt regulation and cotton growth in a salinized field. Treatments included conventional and leaching irrigation quotas, combined with emitter flow rates of 1.4–3.0 L/h and emitter spacings of 20–30 cm. Soil water–salt dynamics, cotton growth, and yield responses were monitored. The results indicated that the irrigation quota was the dominant factor regulating root-zone water retention and salt leaching, with leaching quotas significantly enhancing soil moisture and desalination compared to conventional quotas. Emitter parameters significantly modulated water and salt distribution. A moderate flow rate of 2.4 L/h combined with a 30 cm spacing created more uniform soil moisture distribution and more efficient salt leaching within the root zone. Cotton growth and yield responded markedly to this water–salt regulation pattern. The combination of a 2.4 L/h flow rate, 30 cm spacing, and the leaching quota achieved the highest seed cotton yield of 6343.12 kg/hm². However, increasing the total seasonal irrigation quota reduced irrigation water productivity from 1.26 kg/m³ to 0.97 kg/m³. Based on the experimental data, optimization models for yield and salt leaching index were constructed and used for prediction. The model-predicted parameter combination for theoretical reference was an emitter discharge rate of 2.1–2.4 L/h, a spacing of 30 cm, and a per-application irrigation quota of 60–66 mm, with a predicted yield of 6356.68 kg/hm² and a salt leaching index of 6.48. This study provides a quantitative basis for optimizing drip irrigation system parameters and the management of water and salt in saline cotton fields in the arid region of Xinjiang. Full article

Nanoneedle arrays provide a promising interface for intracellular delivery, yet scalable control of array geometry and membrane penetration mechanics remains insufficiently understood. Here, we developed a rapid and scalable strategy for fabricating geometry-tunable silicon nanoneedle arrays. One-step SF₆/O₂ etching produced ordered arrays with center-to-center spacing of 1–5 μm, whereas pseudo-Bosch etching produced high-aspect-ratio (HAR) nanoneedles. Using a microwell-assisted cell-on-probe atomic force microscopy platform, we quantified the first penetration force, penetration probability, and number of penetration events at the single-cell level. For one-step-etching arrays, increasing spacing from 1 to 5 μm reduced the first penetration force from 34.87 ± 2.90 to 4.05 ± 0.30 nN and increased the penetration probability from 0.21 ± 0.03 to 0.87 ± 0.04. A phenomenological inverse-square model captured the force–spacing relationship, supporting an array-level load-sharing mechanism. Under identical vibration-assisted microfluidic conditions, the FITC-dextran-positive fraction increased from 22.8% for 1 μm arrays to 77.1% for 5 μm arrays, whereas 1 μm HAR arrays achieved 28.8%. These results identify array spacing as a key factor governing single-cell penetration and delivery and provide a mechanistic basis for nanoneedle-based biosensor and cell-interface design. Full article

The blind hyperspectral unmixing of altered minerals holds significant indicative importance for the exploration of sandstone-type uranium deposits. Nevertheless, the distinctive backscattering and multiphase characteristics are typically underestimated by existing methods, resulting in limited accuracy in the unmixing performance of complex mixtures. This study presents a physics-informed Swin Transformer Network (PIST−Net) for the blind hyperspectral unmixing of hematite, goethite, biotite, and chlorite. The proposed model includes a lightweight Swin Transformer encoder for the long-range modeling of spatial and spectral features. Furthermore, a dual-branch decoder with adaptive physical parameters was introduced in the spectral reconstruction section. In this decoder, the backscattering prediction head independently estimates the backscattering factor for each endmember, accounting for the true behavior of the mineral materials. As a key parameter of the Hapke model, the learnable strategy can reduce the spectral error in the single-scattering albedo (SSA) space to improve the accuracy of abundance estimation. The results show that PIST−Net consistently outperformed all six competing models on the altered mineral (AM) dataset and the NASA Reflectance Experiment Laboratory (RELAB) dataset. For mixtures composed of 2–4 endmembers, the mean RMSE of estimated abundances ranges from 0.0312 to 0.1037. Full article