Search Results (35,775)

Background: Perioperative factors significantly impact oncologic outcomes after radical cystectomy (RC) for bladder cancer. This study aimed to identify key perioperative predictors for overall (OS) and progression-free survival (PFS) and to develop a prognostic nomogram for the identification of high-risk patients adapted to the clinical routines and standard of care of our country. Methods: We retrospectively analyzed 121 patients undergoing RC (2014–2024). Data on patient demographics, comorbidities, tumor pathology, neoadjuvant treatments, extensive intraoperative factors, and postoperative events were assessed using COX models. A prognostic nomogram for 3-year OS was constructed. Results: Median follow-up was 44.33 months. Significant predictors for worse OS included lymphovascular invasion (LVI) (HR 2.22), higher T stage (HR 8.75), N+ status (HR 1.10), and intraoperative complications (HR 3.04). Similar predictors were noted for PFS. The developed nomogram incorporated T-, N-stages, sex, grade, intraoperative complications and early (12 months) recurrence, and was able to significantly identify patients with a higher mortality risk (p < 0.001) with a C-index of 0.74. Conclusions: Our nomogram for mortality prediction of BC patients offers a promising tool for individualized risk stratification. Further studies are required for its external validation. Full article

With the expansion of offshore and subsea infrastructure in Arctic and sub-Arctic regions, concerns are rising, driven by climate change and global warming, over the risk of drifting icebergs colliding with these structures in cold waters. Traditional methods for estimating iceberg underwater height and assessing subgouge soil properties, such as costly and time-consuming underwater surveys or centrifuge tests, are still used, but the industry continues to seek faster and more cost-efficient solutions. In this study, the extra tree regression (ETR) algorithm was employed for the first time to simultaneously model iceberg drafts and subgouge soil properties in both sandy and clay seabeds. The ETR approach first predicted the iceberg draft, then simulated subgouge soil reaction forces and deformations. A total of 22 ETR models were developed, incorporating parameters relevant to both iceberg draft estimation and subgouge soil characterization. The best-performing ETR models, along with the most influential input variables, were identified through a combination of sensitivity, error, discrepancy, and uncertainty analyses. The ETR model predicted iceberg draft with a high level of accuracy (R = 0.920, RMSE = 1.081), while the superior model for vertical reaction force in sand achieved an RMSE of 43.95 with 70% of predictions within 16% error. The methodology demonstrated improved prediction capacity over traditional techniques and can serve early-stage iceberg risk management. Full article

The accurate identification of rice growth stages is critical for precision agriculture, crop management, and yield estimation. Remote sensing technologies, particularly multimodal approaches that integrate high spatial and hyperspectral resolution imagery, have demonstrated great potential in large-scale crop monitoring. Multimodal data fusion offers complementary and enriched spectral–spatial information, providing novel pathways for crop growth stage recognition in complex agricultural scenarios. However, the lack of publicly available multimodal datasets specifically designed for rice growth stage identification remains a significant bottleneck that limits the development and evaluation of relevant methods. To address this gap, we present RiceStageSeg, a multimodal benchmark dataset captured by unmanned aerial vehicles (UAVs), designed to support the development and assessment of segmentation models for rice growth monitoring. RiceStageSeg contains paired centimeter-level RGB and 10-band multispectral (MS) images acquired during several critical rice growth stages, including jointing and heading. Each image is accompanied by fine-grained, pixel-level annotations that distinguish between the different growth stages. We establish baseline experiments using several state-of-the-art semantic segmentation models under both unimodal (RGB-only, MS-only) and multimodal (RGB + MS fusion) settings. The experimental results demonstrate that multimodal feature-level fusion outperforms unimodal approaches in segmentation accuracy. RiceStageSeg offers a standardized benchmark to advance future research in multimodal semantic segmentation for agricultural remote sensing. The dataset will be made publicly available on GitHub v0.11.0 (accessed on 1 August 2025). Full article

Due to their low permeability, the location of natural fractures is key to the successful development of buried hill reservoirs. Due to the high degree of rock fragmentation and strong absorption of seismic waves at the top of buried hill formations, it is hard to identify the distribution of natural fractures inside a buried hill using conventional seismic methods. To overcome this difficulty, this study proposes a natural fracture identification technology for buried hill reservoirs that combines a continuum damage mechanics model with finite element numerical simulation. A 3D numerical solution workflow is established to determine the natural fracture distribution in target buried hill reservoirs. By constructing a geological model of a block, reconstructing the orogenic history, developing a 3D finite element model, and performing numerical simulations, the multi-stage orogenic processes experienced by buried hill reservoirs and the resultant natural fracture formation are replicated. This approach yields 3D numerical results of natural fracture distribution. Using the G-Block in the Zhongyuan Oilfield as a case study, the natural fracture distribution in a buried hill reservoir composed of mixed lithologies, including marble and Carboniferous formations, within the faulted G6-well group is analyzed. The results include plane views of the contour of damage variable SDEG, which represents the fracture distribution within the subsurface layer at 600 m intervals below the buried hill surface, as well as a vertical sectional view of the contour of SDEG’s distribution along specified well trajectories. By comparison with the results of the fracture distribution obtained with logging data, a consistency of 87.5% is achieved. This indicates the reliability of the numerical results for natural fractures obtained using the technology presented here. Full article

Prime editing (PE), a novel “search-and-replace” genome editing technology, demonstrates significant potential for crop genetic improvement due to its precision and versatility. However, since its initial application in plants, PE technology has consistently faced challenges of low and variable editing efficiency, representing a major bottleneck hindering its broader application. Therefore, this study conducted a systematic review following the PRISMA 2020 guidelines. We systematically searched databases—Web of Science, PubMed, and Google Scholar—for studies published up to June 2025 focusing on enhancing PE performance in crops. After a rigorous screening process, 38 eligible primary research articles were ultimately included for comprehensive analysis. Our analysis revealed that early PE systems such as PE2 could perform diverse edits, including all 12 base substitutions and small insertions or deletions (indels), but their efficiency was highly variable across species, targets, and edit types. To overcome this bottleneck, researchers developed four major optimization strategies: (1) engineering core components such as Cas9, reverse transcriptase (RT), and editor architecture; (2) enhancing expression and delivery via optimized promoters and vectors; (3) improving reaction processes by modulating DNA repair pathways or external conditions; and (4) enriching edited events through selectable or visual markers. These advancements broadened PE’s targeting scope with novel Cas9 variants and enabled complex, kilobase-scale DNA insertions and rearrangements. The application of PE technology in plants has evolved from basic functional validation, through systematic optimization for enhanced efficiency, to advanced stages of functional expansion. This review charts this trajectory and clarifies the key strategies driving these advancements. We posit that future breakthroughs will increasingly depend on synergistically integrating these strategies to enable the efficient, precise, and predictable application of PE technology across diverse crops and complex breeding objectives. This study provides an important theoretical framework and practical guidance for subsequent research and application in this field. Full article

Understanding the mechanisms governing forest community assembly across different growth stages is essential for revealing succession dynamics and guiding forest restoration. While much attention has been given to overstory trees, the understory regeneration layer, critical for forest succession, remains less explored, particularly regarding its stage-specific survival strategies and assembly processes. This study investigates the natural regeneration of Quercus variabilis forests in northern China, focusing on the transition from early to later growth stages. Our objectives were to (1) identify the phylogenetic and functional structures of regeneration communities at early and later stages, (2) explore their responses to environmental gradients, and (3) assess the roles of deterministic and stochastic processes in shaping community assembly. We integrated phylogenetic structure, functional traits, and environmental gradients to examine natural regeneration communities. The results revealed clear stage-dependent patterns: communities exhibited random phylogenetic and functional structures in the early growth stage, suggesting a dominant role of stochastic processes during early recruitment. In contrast, communities showed phylogenetic clustering and functional overdispersion in later growth stages, indicating the increasing influence of environmental filtering and interspecific competition as individuals developed. Generalized Dissimilarity Modeling (GDM) further revealed that dispersal limitation and pH were key predictors of phylogenetic β-diversity in the later growth stage, while total phosphorus drove functional β-diversity in the later growth stage. No significant predictors were found for β-diversity in the early stage. These findings highlight the shift from stochastic to deterministic processes during forest regeneration, emphasizing the stage-dependent nature of assembly mechanisms. Our study elucidates the stage-specific assembly rules of Q. variabilis forests and offers theoretical guidance for stage-targeted interventions in forest management to promote positive succession. Full article

To meet the requirements of geometric nonlinear modeling and bending–torsion coupling analysis of long, flexible offshore blades, this paper develops a high-precision engineering simplified model based on the Absolute Nodal Coordinate Formulation (ANCF). The model considers nonlinear variations in linear density, stiffness, and aerodynamic center along the blade span and enables efficient computation of 3D nonlinear deformation using 1D beam elements. Material and structural function equations are established based on actual 2D airfoil sections, and the chord vector is obtained from leading and trailing edge coordinates to calculate the angle of attack and aerodynamic loads. Torsional stiffness data defined at the shear center is corrected to the mass center using the axis shift theorem, ensuring a unified principal axis model. The proposed model is employed to simulate the dynamic behavior of wind turbine blades under both shutdown and operating conditions, and the results are compared to those obtained from the commercial software Bladed. Under shutdown conditions, the blade tip deformation error in the y-direction remains within 5% when subjected only to gravity, and within 8% when wind loads are applied perpendicular to the rotor plane. Under operating conditions, although simplified aerodynamic calculations, structural nonlinearity, and material property deviations introduce greater discrepancies, the x-direction deformation error remains within 15% across different wind speeds. These results confirm that the model maintains reasonable accuracy in capturing blade deformation characteristics and can provide useful support for early-stage dynamic analysis. Full article

This study investigated the effects of light intensity regulation on yield and energy efficiency during potato pre-basic seed propagation in plant factories. Using virus-free ‘Favorita’ potato seedlings as experimental material, gradient light intensities (200, 300, and 400 μmol·m²·s⁻¹) were applied at four developmental stages: the seedling stage (SS), tuber formation stage (TFS), tuber growth stage (TGS), and harvest stage (HS), to explore the physiological mechanisms of stage-specific light intensity regulation and energy utilization efficiency. The results revealed that: (1) The per-plant tuber yield of the high yield group reached 72.91 g (T59 treatment), representing a 25% increase compared to the medium yield group and a 168% increase compared to the low yield group. Additionally, the high yield group exhibited superior leaf area, photosynthetic rate, and accumulation of sucrose and starch. (2) The impact of light intensity on tuber development exhibited stage specificity: low light intensity (200 μmol·m⁻²·s⁻¹) during TFS promoted early tuber initiation, while a high light intensity (400 μmol·m⁻²·s⁻¹) enhanced tuber formation efficiency. Increasing the light intensity during TGS facilitated the accumulation of sucrose and starch in tubers. (3) Energy use efficiency (EUE) increased significantly with yield, with the high yield group reaching 3.2 g MJ⁻¹, representing 52% and 88% improvements over the medium yield (2.1 g MJ⁻¹) and low yield (1.7 g MJ⁻¹) groups, respectively. A “stage-specific precision light supplementation” strategy was proposed, involving moderate light reduction (200 μmol·m⁻²·s⁻¹) during TFS and light enhancement (300 μmol·m⁻²·s⁻¹) during TGS to coordinate source-sink relationships and optimize carbohydrate metabolism. This study provides a theoretical basis for efficient potato production in plant factories. Full article

The destabilizing damage of rock structures in coal beds engineering is greatly influenced by the bearing rupture features and energy evolution laws of rock–coal assemblages with varying height ratios. In this study, we used PFC3D to create rock–coal assemblages with rock–coal height ratios of 2:8, 4:6, 6:4, and 8:2. Uniaxial compression simulation was then performed, revealing the expansion properties and damage crack dispersion pattern at various bearing phases. The dispersion and migration law of cemented strain energy zoning; the size and location of the destructive energy level and its spatiotemporal evolution characteristics; and the impact of height ratio on the load-bearing characteristics, crack extension, and evolution of multiple energies (strain, destructive, and kinetic energies) were all clarified with the aid of a self-developed destructive energy and strain energy capture and tracking Fish program. The findings indicate that the assemblage’s elasticity modulus and compressive strength slightly increase as the height ratio increases, that the assemblage’s cracks begin in the coal body, and that the number of crack bands inside the coal body increases as the height ratio increases. Also, the phenomenon of crack bands penetrating the rock through the interface between the coal and rock becomes increasingly apparent. The total number of cracks, including both tensile and shear cracks, decreases as the height ratio increases. Among these, tensile cracks are consistently more abundant than shear cracks, and the proportion between the two types remains relatively stable regardless of changes in the height ratio. The acoustic emission ringing counts of the assemblage were not synchronized with the development of bearing stress, and the ringing counts started to increase from the yield stage and reached a peak at the damage stage (0.8${\sigma }_{\mathrm{c}}$) after the peak of bearing stress. The larger the rock–coal height ratio, the smaller the peak and the earlier the timing of its appearance. The main body of strain energy accumulation was transferred from the coal body to the rock body when the height ratio exceeded 1.5. The peak values of the assemblage’s strain energy, destructive energy, and kinetic energy curves decreased as the height ratio increased, particularly the energy amplitude of the largest destructive energy event. In order to prevent and mitigate engineering disasters during deep mining of coal resources, the research findings could serve as a helpful reference for the destabilizing properties of rock–coal assemblages. Full article

Mining activities have long-term impacts on the environment even after the active stage. Historical mines developed in the 19th and 20th centuries for tin, copper, and mainly iron ore are located in the Pitkäranta area (Karelia, Russia). These objects are considered in our research as natural–anthropogenic sites of long-term water–rock interaction. Waters from flooded mines are the subject of this research. Redox conditions, pH, dissolved oxygen content, conductivity, and water temperature were determined during field work. The chemical composition of natural waters was determined by ICP-MS, ICP-AES, ion chromatography, potentiometric titration, and spectrophotometry. Our investigation showed that the mine waters are fresh and predominantly calcium–magnesium hydrocarbonate; most samples showed elevated sulfate ion contents. Circumneutral pH values and the absence of extremely high concentrations of heavy metals indicate neutral mine drainage. However the calculation of the accumulation coefficient showed the highest levels for siderophile elements relative to the corresponding data of the geochemical regional background. Moreover, zinc has the highest content in the series of heavy metal(loid)s considered. The maximum concentration of zinc was determined in the water of one of the shafts of the Lupikko mine, i.e., 5205 µg/L. The accumulation of heavy metals occurs in the process of long-term interaction of water–rock–organic matter under conductive redox conditions. Overall, the research highlighted the relevance of investigating the geochemistry of historical mines in the Pitkäranta area both from the perspective of environmental safety and the preservation of mining sites for scientific and educational purposes. Full article

Innovation-driven labor markets play a pivotal role in economic development, yet significant disparities exist in how efficiently countries transform innovation inputs into labor market outcomes. This study addresses the critical gap in benchmarking multi-stage innovation efficiency by developing an integrated framework combining Data Envelopment Analysis (DEA) Super Slack-Based Measure (Super-SBM) for static efficiency evaluation and the Malmquist Productivity Index (MPI) for dynamic productivity decomposition, enhanced with cooperative game theory for robustness testing. Focusing on the top 20 innovative economies over a 5-year period, we analyze key inputs (Innovation Index, GDP, trade openness) and outputs (labor force, unemployment rates), revealing stark efficiency contrasts: China, Luxembourg, and the U.S. demonstrate optimal performance (mean scores > 1.9), while Singapore and the Netherlands show significant underutilization (scores < 0.4). Our results identify a critical productivity shift period (average MPI = 1.325) driven primarily by technological advancements. This study contributes a replicable, data-driven model for cross-domain efficiency assessment and provides empirical evidence for policymakers to optimize innovation-labor market conversion. The methodological framework offers scalable applications for future research in computational economics and productivity analysis. Full article

Background: Tuberculosis (TB) remains a major global health challenge, including in Thailand, where both asymptomatic and symptomatic cases sustain transmission. The disease burden increases treatment complexity and mortality, requiring integrated care and coordinated policies. Methods: We developed a deterministic compartmental model to examine the transmission dynamics of TB in Thailand, incorporating both latent and active stages of infection, as well as vaccination coverage. The model was calibrated using national TB incidence data, and sensitivity analysis revealed that the TB transmission rate was the most influential parameter affecting the basic reproduction number (R₀). We evaluated the impact of several intervention strategies, including increased treatment coverage for latent and active TB infections and improved vaccination rates. Results: Our analysis indicates that among the single interventions, scaling up effective treatment for latent TB infections produced the greatest reduction in asymptomatic and symptomatic cases, while enhanced treatment for active TB cases was second most effective for reducing both asymptomatic and symptomatic cases. Importantly, our results indicate that combining multiple interventions yields significantly greater reductions in overall TB incidence than any single approach alone. Our findings suggest that a modest investment in integrated TB control can substantially reduce TB transmission and disease burden in Thailand. However, complete eradication of TB would require a comprehensive and sustained investment to achieve near-universal coverage of both preventive and curative strategies. Conclusions: TB remains a significant public health threat in Thailand. Targeted interventions and integrated strategies are key to reducing disease burden and improving treatment outcomes. Full article

As intelligent vessel technology moves from the proof-of-concept stage to engineering applications, the performance testing and evaluation of autonomous collision avoidance algorithms have become core issues for safeguarding maritime traffic safety. The International Maritime Organization (IMO)’s Maritime Safety Committee (MSC), at its 109th session, agreed to a revised road map for the development of the Maritime Autonomous Surface Ships (MASS) Code; the field has experienced the development stages of single-vessel collision avoidance validation based on COLREGs, multimodal algorithm collaborative testing, and the current construction of a progressive validation system for the integration of a mix of virtual reality and actual reality. In recent years, relevant studies have achieved research achievements, especially in the compatibility of COLREGs and in accurate collision avoidance in complex situations, and other algorithm tests and evaluations have made great breakthroughs. However, a systematic literature review is still lacking. In this paper, we systematically review the research progress of autonomous collision avoidance performance testing and the evaluation of intelligent vessels; summarize the advantages and disadvantages of virtual testing, model testing, and full-scale vessel testing; and analyze the applicability and limitations of mainstream algorithms such as the velocity obstacle algorithm, the artificial potential field algorithm, and reinforcement learning. It focuses on the key technologies such as diverse scene generation, local scene slicing, and the construction of an evaluation index system. Finally, this paper summarizes the challenges faced by autonomous collision avoidance performance testing and the assessment of intelligent vessels and proposes potential technical solutions and future development directions in terms of virtual–real fusion testing, dynamic evaluation index optimization, and multimodal algorithm co-validation, aiming to provide a reference for the further development of this field. Full article

Advanced Persistent Threats (APTs) pose significant cybersecurity challenges due to their multi-stage complexity. Knowledge graphs (KGs) effectively model APT attack processes through node-link architectures; however, the scarcity of high-quality, annotated datasets limits research progress. The primary challenge lies in balancing annotation cost and quality, particularly due to the lack of quality assessment methods for graph annotation data. This study addresses these issues by extending existing APT ontology definitions and developing a dynamic, trustworthy annotation framework for APT knowledge graphs. The framework introduces a self-verification mechanism utilizing large language model (LLM) annotation consistency and establishes a comprehensive graph data metric system for problem localization in annotated data. This metric system, based on structural properties, logical consistency, and APT attack chain characteristics, comprehensively evaluates annotation quality across representation, syntax semantics, and topological structure. Experimental results show that this framework significantly reduces annotation costs while maintaining quality. Using this framework, we constructed LAPTKG, a reliable dataset containing over 10,000 entities and relations. Baseline evaluations show substantial improvements in entity and relation extraction performance after metric correction, validating the framework’s effectiveness in reliable APT knowledge graph dataset construction. Full article

Non-nutritive sucking (NNS) is an instinctive behavior in newborns, consisting of two stages: sucking and expression. It plays a critical role in preparing the infant for oral feeding. In neonatal and pediatric units, NNS assessment is routinely performed to determine feeding readiness. However, these evaluations are often subjective and rely heavily on the clinician’s experience. While other medical devices that support the development of NNS skills exist, they are not specifically designed for the comprehensive assessment of NNS, and their high cost limits accessibility for many hospitals and tertiary care units globally. This paper presents the development and pilot testing of a low-cost, portable device and accompanying software for assessing NNS in newborns hospitalized in neonatal care units. Methods: The device uses force-sensitive resistors to capture expression pressure and a differential pressure sensor to measure NNS. Data were acquired through the analog–digital converter of a microcontroller and transmitted via Bluetooth for real-time graphical analysis. Pilot testing was conducted with six hospitalized preterm newborns, measuring intensity, number of bursts, and sucks per burst. Results demonstrated that the system reliably captures both stages of NNS. Significance: This device provides an affordable, portable solution to support clinical decision-making in clinical units, facilitating accurate, objective monitoring of feeding readiness and developmental progression. Full article