Search Results (4,889)

Introduction: Clinical staging based on digital rectal examination is imprecise, leading to pathological upstaging in patients with prostate cancer (PCa). Accurate preoperative assessment remains a challenge despite the use of multiparametric magnetic resonance imaging (mpMRI) and fusion-guided biopsy. This study aims to identify key predictors of upstaging in preoperative patients. Materials and Methods: A retrospective analysis of 924 patients who underwent radical prostatectomy between July 2012 and January 2025 was performed. Variables included prostate-specific antigen, prostate volume, biopsy type, MRI, body mass index and age. Upstaging was defined as ≥pT3 in patients staged clinically as cT1–2. Optimal cut-offs for continuous variables were defined statistically. Multivariable logistic regression was applied to identify independent predictors of upstaging and minor staging upgrading (MSU)—defined as any upward shift in the pathological T stage relative to the clinical T stage. Model performance was evaluated using the area under the Receiver Operating Characteristic (ROC) curve (AUC). Results: Upstaging occurred in 31.9% and MSU in 50.6% of patients. The mean age was 65 years. Cut-off values for PSA density (PSAD) were 0.29 for upstaging and 0.28 for MSU. In the full-cohort model (AUC = 0.628), PSAD (odds ratio (OR) = 2.55), age (OR = 1.04), and hypertension (HT) (OR = 1.47) were associated with upstaging. In PIRADS-based models, PIRADS 5 and PSAD predicted both upstaging (OR = 1.62 and 6.10, respectively; AUC = 0.664) and MSU (OR = 1.75 and 4.67, respectively; AUC = 0.659). MSU was also associated with HT and a lack of fusion biopsy (AUC = 0.622). Conclusions: PSAD and PIRADS 5 lesions are strong determinants of pathological upstaging and MSU in PCa. These factors should be considered in preoperative risk stratification to improve staging accuracy. Despite advances in imaging and biopsy techniques, upstaging remains a common phenomenon, underlining the need for further refinement of diagnostic protocols. Full article

Digital–green synergy development is a critical pathway for promoting comprehensive rural revitalization and high-quality development. Using panel data from 31 Chinese provinces spanning 2012 to 2023, we employ the global entropy weight method, a coupling coordination degree model, kernel density estimation, and an obstacle degree model to systematically analyze the spatiotemporal evolutionary characteristics and obstacle factors underlying this synergy, aiming to provide a scientific basis for regionally differentiated comprehensive rural revitalization. The findings reveal that: (1) Both digitalization and greenization have improved steadily, though the growth rate of greenization lags behind that of digitalization. The level of digital–green synergy development, although initially low, shows continuous growth. (2) Spatially, digital–green synergy development exhibits a pattern of eastern leadership, central catching-up, western transition, and northeastern stagnation. (3) Nationally, the absolute disparity in digital–green synergy development continues to widen, indicating growing polarization. Regionally, the eastern region exhibits multipolarization, the central region shows bipolarization, while the western and northeastern regions display no significant polarization trends. (4) Production digitalization and living greenization are the primary constraints hindering synergy. Based on these findings, we propose targeted policy recommendations to facilitate deeper integration between rural digitalization and greenization, supporting decision-makers in advancing digital–green synergy development. Full article

With the advancement of 3D digital dentistry, accurate 3D tooth segmentation has become increasingly important in orthodontics and computer-aided diagnosis. However, existing supervised approaches heavily rely on exhaustive face-wise annotations and often exhibit limited generalization across complex clinical meshes. Although self-supervised learning offers a promising alternative to alleviate annotation costs, current paradigms remain challenged by sensitivity to data augmentations, suboptimal representation learning in pure masking schemes, and the complex structural characteristics of dental geometry. To address these limitations, we propose Dental-CMAE, a graph-enhanced hierarchical Contrastive masked AutoEncoder framework tailored for 3D tooth segmentation. The framework incorporates a dual-branch masking strategy that leverages graph-based structural priors to generate distinct corrupted views while preserving intrinsic mesh topology, thereby facilitating robust reconstruction. This is integrated with a feature-level contrastive objective designed to enforce semantic consistency between co-masked regions, which enhances representation discriminability without the requirement for negative sample queues. Additionally, the architecture utilizes a hierarchical multi-scale attention mechanism that partitions feature channels into parallel streams, enabling the simultaneous capture of fine-grained morphological variations and the overarching global dental arch context. Extensive experiments demonstrate that our Dental-CMAE consistently outperforms state-of-the-art fully supervised and self-supervised methods across multiple evaluation metrics. Specifically, our framework achieves an Overall Accuracy (OA) of 95.57%, a mean Intersection-over-Union (mIoU) of 88.14%, and a mean Accuracy (mAcc) of 90.85%. Supported by these quantitative findings, our method validates its effectiveness for robust 3D tooth segmentation, highlighting its strong potential to alleviate annotation bottlenecks and improve the reliability of automated 3D digital dental workflows. Full article

This study aims to reveal the control mechanism of key geometric parameters (flange thickness and flange edge thickness) of H-shaped cross-section on the bending performance of UHPC piles. Through conducting bending tests, combined with digital image correlation (DIC) technology and finite element simulation, the mechanical behavior was studied, and based on the principal strain field obtained from DIC, a strain field concentration index was proposed. The results show that: as the load ratio increases, the strain field concentration and the peak value of the mid-span principal strain continuously increase, and the crack evolution changes from dispersed development to localized control; near the limit state, the strain field concentration can reach approximately 0.28, and the peak value of the principal strain increases in an increasing trend, approximately 20% or more. Under the specific conditions of this test, in terms of ductility and energy absorption, when the flange thickness is constant, increasing the flange thickness of the web increases the energy absorption of the component by approximately 6% to 10%, while the ductility coefficient decreases by approximately 9% to 15%; when the web thickness is constant, increasing the flange thickness reduces the ductility coefficient by approximately 21% to 25%, and the energy absorption decreases by approximately 27% to 29%. The strain field concentration can effectively reflect the evolution process of the localization of bending cracks in H-shaped UHPC piles and can be used for quantitative analysis of their ductility degradation and energy absorption characteristics. It should be clarified that this study does not claim to isolate the effect of a single parameter. Full article

The need for Arabic Aspect-Based Sentiment Analysis (ABSA) has grown steadily alongside the expansion of digital content, while the linguistic complexity of Modern Standard Arabic and its diverse dialects introduces significant challenges. However, progress in the field remains constrained by methodological fragmentation, inconsistent task definitions, heterogeneous datasets, and non-standardized evaluation practices. Based on a systematic analysis of 57 studies, this work presents an analytical and interpretive review that moves beyond performance-oriented surveys to examine the methodological foundations of Arabic ABSA research. The review follows a rigorous and transparent study selection process and applies a structured analytical framework to analyze task formulations, dataset characteristics, modeling approaches and evaluation strategies. Our findings reveal persistent challenges, including ambiguous aspect definitions, insufficiently documented annotation protocols, structural annotation biases, and limited robustness across domains and dialects. A heavy reliance on Transformer-based architectures and new Arabic foundation models can create an illusion of progress. Researchers often evaluate these models on small and homogeneous datasets. Consequently, strong in-domain performance obscures limited cross-domain and cross-dialectal generalizability. This study concludes by outlining actionable research directions, emphasizing clearer task standardization, more rigorous annotation guidelines, unified evaluation, and broader dialectal coverage to enhance reproducibility and scalability in Arabic ABSA systems. Full article

Lithium is a rare metal widely used in the renewable energy industry. The Altyn region in Xinjiang, China, contains abundant granitic pegmatite-type lithium resources; however, the deeply incised and complex terrain limits the accuracy of conventional two-dimensional remote sensing approaches for dike identification and segmentation. To address this limitation, a remote sensing segmentation method incorporating terrain information was proposed. A digital elevation model (DEM) derived from LiDAR data, together with its associated topographic factors, was integrated into the Spatial–Spectral Mamba framework to enable the joint utilization of spectral and terrain features. Rather than performing explicit three-dimensional geometric modeling, the proposed approach enhances a two-dimensional segmentation framework by introducing elevation-derived information, allowing the model to capture terrain-related spatial variations of pegmatite dikes. This design enables improved representation of both the planar distribution and terrain-influenced morphological characteristics of dikes under deeply incised conditions. The Xichanggou lithium deposit in the Altyn region is a large-scale, economically valuable pegmatite-type lithium deposit, and was therefore selected as the study area for pegmatite dike segmentation. The results demonstrated that, compared with conventional two-dimensional approaches and representative machine learning methods, the proposed method achieved higher segmentation accuracy in complex terrain. Improvements were also observed in the continuity and spatial consistency of the extracted dike patterns. Field verification indicated that the major pegmatite dikes delineated by the model were highly consistent with their actual surface exposures. Sampling analyses further confirmed the validity and reliability of the identification results. Overall, the terrain-integrated remote sensing segmentation approach exhibited good applicability and robustness under deeply incised and complex geomorphological conditions. Full article

Whether and how digital financial inclusion (DFI) is associated with ecological efficiency (EE) is a critical issue for the sustainable development of the Yangtze River Economic Belt (YREB). Based on panel data from 2011 to 2023, this study measures EE using the PCA-Super SBM model, and employs panel fixed-effects models and mediation models to systematically examine the association, mechanisms, and regional patterns of DFI with EE in the YREB. The findings are as follows: (1) DFI and EE exhibit notable spatiotemporal co-evolution characteristics, with the DFI index increasing nearly 14-fold and the EE level rising by approximately 21.5% over the study period. (2) DFI shows a statistically significant positive association with EE improvement; this finding remains robust after various robustness checks. (3) The association between DFI and EE is partially mediated through four pathways: capital allocation optimization, green technological innovation, industrial structure upgrading, and environmental regulation strengthening, among which green technological innovation is the most prominent mediating pathway. (4) Numerically, the association strength varies across functional zones, being higher in the ecological barrier zone (EBZ) and the coordinated development zone (CDZ) than in the high-quality development zone (HQDZ); however, differences in coefficients across zones are not statistically significant and should be interpreted cautiously. Based on these findings, this study proposes policy recommendations including establishing a DFI-EE linkage platform, implementing differentiated functional-zone strategies, and strengthening cross-basin collaborative governance, thereby providing a reference for the green transformation of the YREB. Full article

Background: The relationship between facial aesthetic procedures and changes in the stomatognathic system has attracted increasing interest, motivating investigations into their functional and structural impacts. This longitudinal observational study analyzed molar bite force and orofacial tissue pressure in adults who underwent hyaluronic acid injections in the mandibular angle. Methods: Ten adults (eight women and two men; mean age 34.3 ± 11.2 years) with normal occlusion and no temporomandibular disorders were included. The MD Codes guided injection points of 2 mL of hyaluronic acid in the mandibular angle. Maximum right and left molar bite force was measured using a digital dynamometer, and tongue, lip, and cheek pressures were measured with a Pro-Fono Biofeedback device. Assessments occurred before and at 15, 30, and 60 days. Repeated measures ANOVA with Bonferroni correction was applied (p < 0.05), and effect sizes and 95% confidence intervals were calculated. Results: No statistically significant differences were observed in maximum molar bite force throughout the follow-up period. Regarding orofacial pressures, a significant main effect of time was observed for tongue pressure (p = 0.03); however, the effect size was moderate-to-large, and values showed considerable variability across participants. Lip and cheek pressures remained stable over time. Conclusions: Hyaluronic acid injection in the mandibular angle did not show clinically detectable changes in maximum molar bite force, suggesting short-term preservation of masticatory function within the 60-day follow-up period. These findings are limited to short-term observations and specific sample characteristics. The observed variation in tongue pressure may reflect adaptive functional adjustments, although variability across participants was considerable. Full article

The rapid evolution of fintech has accelerated the integration of blockchain technology and cryptocurrencies into crowdfunding platforms, reshaping entrepreneurial finance and challenging traditional conceptions of money, intermediation, and financial risk. This study empirically examines the socio-cultural, demographic, and behavioural factors influencing funders’ perceptions and investment decisions in crypto-crowdfunding, an emerging model situated at the intersection of digital currencies, financial inclusion, and decentralised capital formation. Using primary survey data from a focus group of 50 respondents measuring perceptions through a structured five-point Likert questionnaire, the analysis investigates how risk perception, trust and security, investor awareness, and perceived benefits shape participation in crypto-crowdfunded projects. The findings indicate that blockchain-based features such as transparency and decentralisation are associated with variations in perceived trust and risk assessment, rather than uniformly enhancing investor confidence. Socio-demographic characteristics emerge as significant determinants of investor awareness, perceived risks, and expected benefits, confirming pronounced behavioural heterogeneity in digital-finance participation. Regression results reveal strong interdependencies between trust, risk perception, and awareness, underscoring the importance of informational quality and risk-governance mechanisms in supporting sustainable adoption. By providing empirical evidence on individual-level determinants of participation in crypto-crowdfunding, the study contributes to the literature on the future of money by clarifying how crypto-crowdfunding operates as a behavioural-financial phenomenon embedded in decentralised governance structures. Full article

Acoustic emission (AE) and digital image correlation (DIC) techniques enable real-time capture of damage signals and full-field deformation at anchored rock–concrete interfaces under shear loading, which is critical for quantitatively characterizing freeze–thaw (F-T) degradation and preventing geological disasters in cold regions. This study synchronously monitored full-shear-process AE signals using a broadband AE system (150 kHz resonant frequency, 5 MS/s sampling) and captured high-precision full-field deformation via a 5-megapixel monocular DIC system (25 fps). F-T cycle and direct shear tests were conducted on sandstone–concrete anchored specimens with varying F-T cycles and anchor depths to investigate their effects on shear mechanical properties, AE characteristics and failure modes. Results show that AE peak ring count first decreases by 44.9% then increases by 56.5%, while cumulative ring count exhibits a three-stage evolution. Shear crack proportion first decreases then increases, with tensile failure remaining dominant throughout. DIC reveals that F-T cycles shift failure from crack propagation to surface delamination and interface slip, while different anchor depths induce distinct failure patterns. This study confirms that AE and DIC can accurately characterize F-T degradation, providing a reliable non-destructive monitoring method for cold-region anchorage engineering. Full article

In South Korea, 99.7% of international freight is transported through ports. At ports handling massive cargo volumes, prolonged truck waiting times have become a significant social concern. To enhance port operational efficiency and ensure driver safety, systematic congestion management is required, which can be facilitated by predicting truck turnaround time (TAT) in advance. However, existing TAT prediction studies have focused on individual ports where data collection is feasible, limiting the applicability of these models to other ports. The objective of this study was to evaluate the transferability of TAT prediction models to different ports. For the analysis, digital tachograph data capturing the trajectories of heavy-duty trucks were employed. The results indicate that a long short-term memory-based model effectively captures the complex operational characteristics of ports and demonstrates high predictive accuracy at Busan New Port and Busan North Port. By applying transfer learning from the best-performing Busan New Port model, the predictive accuracy for Gunsan Port, a target port with limited data, was substantially improved. This study confirms the feasibility of applying transfer learning in ports with constrained data availability, demonstrating that practical TAT prediction models can be developed under realistic operational constraints. Full article

Under the background of the Rural Revitalization Strategy, Zhejiang Province is promoting “Zhejiang-style Vernacular Dwellings” as a crucial measure to enhance the rural living environment and architectural appearance. However, traditional stylistic control tools, such as standardized rural housing design atlases, exhibit limitations including weak responsiveness to villagers’ individualized needs and high professional thresholds. Consequently, they struggle to address the bottlenecks in grassroots governance efficiency caused by massive and personalized housing demands. Meanwhile, when applied to architectural design, general generative AI technologies often suffer from “structural hallucinations” and the weakening of regional characteristics due to a lack of physical tectonic constraints. Oriented towards the governance requirements of the Zhejiang Provincial Rural Housing Design Guidelines, this study proposes a compliance evaluation-driven “Contour-Semantic-Image” hierarchical generative control framework. This aims to construct a visual scheme generation and pre-screening workflow that deeply adapts to the logic of rural governance. At the data level, this research aggregates multi-source materials, including official standardized atlases, government stylistic guidelines, and real-world photographs. Through expert screening and standardized processing of 596 schemes, a dataset of 333 high-quality, finely annotated structured samples is constructed. Furthermore, a human-guided, machine-segmented workflow assisted by Segment Anything Model 2 (SAM 2) is employed to establish a semantic label system comprising 4 major categories and 13 subcategories of components, thereby achieving the structural deconstruction of architectural prior knowledge. At the generation level, a two-stage model is trained based on Stable Diffusion and ControlNet: Stage I utilizes contour conditions and “layout prompts” to generate semantic label maps, aiming to strengthen component topology and layout consistency; Stage II employs the semantic label maps and “style prompts” as conditions to generate photorealistic facade images. By utilizing explicit semantic constraints to guide the model from pixel synthesis to logical generation, it achieves the controllable rendering of stylistic details and material expressions. At the evaluation level, an automated verification system featuring “clause translation–metric calculation–comprehensive scoring” is proposed. It conducts scoring, re-ranking, and diagnostic feedback on the generated variants across three dimensions: Design Rationality (Q), General Compliance (G), and Jiangnan water-town Regional Characteristics (P-J), forming a closed-loop “Generation-Evaluation-Feedback” workflow. Overall, this framework provides a “visualizable, evaluable, and explainable” pathway for scheme generation and pre-screening in the digital governance of rural architectural appearance. Full article

Although polylactic acid (PLA) and acrylonitrile–butadiene–styrene (ABS) are among the most widely used polymers in material extrusion, their limited toughness and energy-absorption capacity often restrict the structural performance of 3D-printed functional components. To address the limited comparative understanding of how thermoplastic polyurethane (TPU) modifies the deformation behavior and phase characteristics of these two polymer systems, this study presents a multi-analytical evaluation of TPU-reinforced PLA and ABS blends. To this end, both polymers were blended with TPU at 10–50 wt% and processed into filaments via single-screw extrusion. The resulting filaments were used to fabricate ASTM D638 Type I tensile specimens via material extrusion under matrix-specific, but internally consistent, printing parameters. For each composition, five specimens were tested to obtain representative values of tensile strength, elongation at break, and toughness. In addition to conventional tensile testing, the evolution of strain during deformation was monitored using digital image correlation (DIC), enabling full-field characterization of local deformation behavior. To ensure experimental reliability, specimen masses were carefully controlled, and the datasets were analyzed using MATLAB. Thermal properties were investigated by differential scanning calorimetry (DSC) to determine the influence of TPU on glass transition, melting behavior, and phase mobility, and to relate these thermal characteristics to the mechanical response of the blends. The incorporation of TPU significantly increased ductility and energy absorption in both polymer matrices, although the magnitude of improvement differed. ABS/TPU blends exhibited the highest toughness enhancement, reaching 221.4% at 30 wt% TPU, while PLA/TPU systems showed nearly a twofold increase at 20 wt% TPU. DIC analysis further revealed a transition from localized brittle deformation in neat polymers to more distributed plastic deformation with increasing TPU content. DSC results indicated reduced crystallinity in PLA-rich blends and enhanced segmental mobility in ABS-based systems, consistent with the observed mechanical behavior. Overall, the combined mechanical, optical, and thermal analyses demonstrate that the optimal TPU content is matrix-dependent, providing practical guidelines for tailoring PLA- and ABS-based filaments to achieve a controlled balance between stiffness, ductility, and energy absorption in material extrusion applications. Full article

Background/Objectives: Brain health monitoring is increasingly essential as modern cognitive load, stress, and lifestyle pressures contribute to widespread neural instability. The paper presents BrainTwin, a next-generation cognitive digital twin, as a patient-specific, constantly updating computer model that combines state-of-the-art MRI analytics for neuro-oncological assessment related to clinical study and management of tumors affecting the central nervous system (including their detection, progression, and monitoring) with real-time EEG-based brain health intelligence. Methods: Structural analysis is driven by an Enhanced Vision Transformer (ViT++), which improves spatial representation and boundary localization, achieving more accurate tumor prediction than conventional models. The extracted tumor volume forms the baseline for short-horizon tumor progression modeling. Parallel to MRI analysis, continuous EEG signals are captured through an in-house wearable skullcap, preprocessed using Edge AI on a Hailo Toolkit-enabled Raspberry Pi 5 for low-latency denoising and secure cloud transmission. Pre-processed EEG packets are authenticated at the fog layer, ensuring secure and reliable cloud transfer, enabling significant load reduction in the edge and cloud nodes. In the digital twin, EEG characteristics offer real-time functional monitoring through dynamic brainwave analysis, while a BiLSTM classifier distinguishes relaxed, stress, and fatigue states, which are probabilistically inferred cognitive conditions derived from EEG spectral patterns. Unlike static MRI imaging, EEG provides real-time brain health monitoring. The BrainTwin performs EEG–MRI fusion, correlating functional EEG metrics with ViT++ structural embeddings to produce a single risk score that can be interpreted by clinicians to determine brain vulnerability to future diseases. Explainable artificial intelligence (XAI) provides clinical interpretability through gradient-weighted class activation mapping (Grad-CAM) heatmaps, which are used to interpret ViT++ decisions and are visualized on a 3D interactive brain model to allow more in-depth inspection of spatial details. Results: The evaluation metrics demonstrate a BiLSTM macro-F1 of 0.94 (Precision/Recall/F1: Relaxed 0.96, Stress 0.93, Fatigue 0.92) and a ViT++ MRI accuracy of 96%, outperforming baseline architectures. Conclusions: These results demonstrate BrainTwin’s reliability, interpretability, and clinical utility as an integrated digital companion for tumor assessment and real-time functional brain monitoring. Full article

In the era of digital economy, manufacturing industry transcends geographical space to build virtual networks. Revealing the complex network characteristics and driver paths of virtual agglomeration is of great significance for accelerating the digitalization of manufacturing. First, this paper explains the formation mechanism and proposes the model of virtual agglomeration; moreover, the paper identifies complex network characteristics. Finally, this paper constructs a driving path framework based on the “Technology–Organization–Environment” theory, and uses fuzzy set qualitative comparative analysis to identify paths. The results show that the technological platform foundation plays a core role in enhancing the level of virtual agglomeration. Differentiated combinations of organizational and environmental conditions also have a positive impact. This study provides theoretical support and practical reference for cities to accelerate virtual agglomeration according to local conditions. Full article