Search Results (4,286)

In situ-reconstruction and expansion of existing tunnels in complex urban areas involve simultaneous construction and traffic operation, while pre-existing structural defects may further amplify deformation and safety risks under excavation disturbance. To address this issue, this study develops a dynamic deformation-risk assessment framework for the traffic-bearing side of reconstructed tunnels by integrating defect-induced structural deterioration, excavation-stage deformation responses, and cloud-model-based uncertainty characterization. A three-dimensional finite-difference model was established for an in situ-tunnel reconstruction project in China, in which an elastic modulus weakening coefficient was introduced to represent the mechanical deterioration associated with existing defects. Field monitoring was used to validate the numerical model. At an excavation advance of 47.0 m, the measured and simulated crown settlements were 2.40 and 2.73 mm, respectively, with a relative deviation of 13.75%, while differences at more than 80% of the excavation nodes were within 1 mm. The dynamic assessment showed that the membership degrees of crown settlement and clearance convergence to Grade 2 were 0.418 and 0.406, respectively, with neither indicator entering the Grade 4–5 high-risk range. Clearance convergence entered Grade 2 earlier during excavation and was therefore identified as a priority monitoring indicator for subsequent construction. Numerical simulation was used to obtain and validate the excavation-induced deformation responses, whereas the cloud model was used to classify the corresponding dynamic risk state; no independent long-term deformation forecasting was performed in this study. Full article

The space–air–ground integrated network (SAGIN) enhanced by mobile edge computing (MEC) has emerged as a promising architecture for future 6G systems, providing wide-area coverage and distributed computing capabilities. By representing requests as service function chains (SFCs), network function virtualization (NFV) enables coordinated orchestration of underlying resources. However, SFC orchestration in SAGIN-MEC faces three significant challenges, including multi-layer resource heterogeneity, topology dynamics, and complex sequential dependencies within SFCs. To address these challenges, this paper proposes GT-PPO, a deep reinforcement learning (DRL)-based approach for online SFC orchestration designed to maximize network profit while minimizing end-to-end (E2E) delay. GT-PPO employs a graph attention network (GAT) to identify interactions among heterogeneous nodes and extract rich feature information from the dynamic physical network. Additionally, it leverages the Transformer self-attention mechanism to encode the SFC context based on resource demands and current deployment progress, thereby capturing global dependencies among virtual network functions (VNFs). Extensive simulation results demonstrate that, under high-load conditions, GT-PPO outperforms representative baselines, increasing the request acceptance ratio and network profit by 5.62% and 16.71%, respectively, while reducing the average E2E delay by 15.46%. Full article

Prostate cancer is the prototypical immunologically cold solid tumor, with objective response rates of only 3–5% to immune checkpoint inhibitors in unselected metastatic castration-resistant disease and an estimated 89.8% of tumors classified as immunologically ignorant. Three convergent features sustain this phenotype: low tumor mutational burden with defective Major Histocompatibility Complex class I antigen presentation; an immunosuppressive microenvironment dominated by regulatory T-cells, myeloid-derived suppressor cells, and M2 macrophages; and dense stromal and vascular barriers that exclude effector lymphocytes. Across these mechanisms, one compartment has received disproportionately little attention: the lymphatic system. Tumor-associated lymphatic remodeling driven by vascular endothelial growth factor C and D produces structurally abnormal vessels that impair antigen and dendritic cell trafficking to tumor-draining lymph nodes; the lymph nodes themselves are reprogrammed to a tolerogenic state by lymphatic endothelial cells expressing programmed death-ligand 1 and lacking costimulation and by regulatory T-cells that suppress effector egress. This review synthesizes evidence that in prostate cancer, immune coldness reflects not merely a problem of checkpoint engagement with the tumor but a failure of antigen trafficking at the lymphatic interface. We discuss therapeutic strategies that target this axis, such as lymphangiogenesis-inducing vaccines, lymph-node-directed checkpoint delivery, induction of intratumoral tertiary lymphoid structures and stromal reprogramming, as complements to existing immunotherapy. Targeting trafficking, not only checkpoints, may be the prerequisite for converting PCa from cold to hot. Full article

Background: Organized cervical cancer screening aims to reduce the burden of advanced disease through earlier detection. However, a substantial proportion of women continue to present with locally advanced cervical cancer (LACC) requiring definitive chemoradiation. This study evaluated temporal changes in the demographic and clinicopathological characteristics of women with LACC treated with definitive chemoradiation before and after implementation of the organized cervical cancer screening program in Serbia. Methods: This retrospective single-center cohort study included 200 consecutive women with histologically confirmed LACC treated with definitive chemoradiation at the Institute of Oncology and Radiology of Serbia. Two cohorts were analyzed: a pre-screening cohort (2010–2011, n = 100) and a post-screening cohort (2022–2023, n = 100). Demographic and clinicopathological characteristics, including age, histopathology, FIGO stage, lymph node involvement, and geographic distribution, were compared. For study purposes, all patients were retrospectively restaged according to the FIGO 2018 classification using the best available clinical, radiological, and pathological data. Results: Women in the post-screening cohort were significantly older at diagnosis than those in the pre-screening cohort (mean age 54.9 vs. 49.7 years, p = 0.0046), with a substantially higher proportion of patients older than 64 years (28% vs. 2%, p < 0.0001). Although the overall distribution of FIGO stages II–IV did not differ significantly, a marked redistribution of FIGO 2018 substages was observed (p < 0.0001), characterized by an increased proportion of stage IIIC disease and significantly more frequent lymph node involvement (63% vs. 41%, p = 0.0018). Geographic distribution remained stable, with most patients referred from the Belgrade administrative district. Conclusions: This study demonstrated clinically relevant temporal changes in the demographic and clinicopathological characteristics of women requiring definitive chemoradiation for LACC in Serbia. More than a decade after implementation of organized cervical cancer screening, tertiary referral centers continue to manage a substantial burden of patients with locally advanced disease requiring complex, resource-intensive treatment. These findings should not be interpreted as a direct evaluation of the national screening program but may provide valuable insights for healthcare planning in Serbia and other countries with similarly resource-constrained healthcare systems. Strengthening participation in organized screening, ensuring timely diagnostic evaluation, and improving HPV vaccination uptake remain essential to reduce the burden of advanced cervical cancer. Full article

Lymphedema is a chronic, progressive disorder caused by impaired lymphatic drainage. It leads to swelling, inflammation, fibrosis, increased infection risk, and reduced quality of life. Diagnosis is primarily clinical and is supported by limb measurements, functional assessment, and imaging. Ultrasonography helps exclude mimicking conditions, lymphoscintigraphy assesses lymphatic transport, and magnetic resonance lymphangiography defines lymphatic anatomy to guide treatment planning. Management should be multidisciplinary and tailored to disease stage. Complex decongestive therapy remains the first-line treatment, while surgical options such as lymphaticovenular anastomosis, vascularized lymph node transfer, and liposuction are increasingly used in selected patients. Early recognition and individualized treatment are essential for durable disease control. This article reviews the current evidence on the diagnosis, imaging, and management of lymphedema and highlights recent developments in the field. Full article

Complex underwater environments induce difficult-to-quantify ranging errors, constraining the localization accuracy and robustness of heterogeneous networks. To address this, a node localization method based on a Geometric Topology Perception-Enhanced Multi-Stage Reinforcement Learning Strategy is proposed. First, an uncertainty quantification model under multi-source interference is established to characterize time-varying noise and accurately quantify the ranging errors of heterogeneous links. Subsequently, using the resulting ranging variance, an adaptive weight allocation mechanism based on Minimum Variance Unbiased Estimation is constructed to dynamically adjust link weights, achieving the robust fusion of multi-modal observation data. Finally, a Weighted Least Squares objective function is formulated, and the GP-AC strategy is developed. By utilizing Gaussian Process Regression and local Geometric Dilution of Precision, a multi-stage reward mechanism is constructed to circumvent topological traps and accurately estimate the single-epoch three-dimensional coordinates of static or quasi-static underwater sensor nodes. Simulation results demonstrate that system robustness is improved by 91.9%, average accuracy is enhanced by 54.9%, and the measured average localization time is 7.45 s. Full article

This study develops a SCHISM-based hydrodynamic model and an offline Lagrangian virtual-particle workflow for the Zadar Channel, a geometrically complex island–mainland passage in the eastern Adriatic. Independent hourly observations from the MP Zadar tide gauge operated by the Hydrographic Institute of the Republic of Croatia (HHI) were used to evaluate the modelled free-surface response. After exclusion of the first 24 h ramping period, 192 matched hourly pairs gave a Pearson correlation of 0.913, a mean bias of 0.012 m, a mean absolute error of 0.051 m, and a root-mean-square error of 0.070 m; cross-correlation was maximized at zero lag. The model reproduced the timing of the observed oscillations but underestimated their amplitude, with simulated and observed standard deviations of 0.117 and 0.157 m, respectively. The adopted unstructured mesh contains 16,962 triangular elements and 9081 nodes. In four 24 h particle-sensitivity tests, maximum reach ranges from 8.4 to 14.2 km; a 15-fold change in horizontal diffusivity affects reach less than sampling a lower model layer, which reduces reach by 32.8%. In the June 2025 event calculation, cumulative numerical shoreline contact increases from zero to all 1000 particles. The approximately 4 km Copernicus regional product masks the narrow interior passages and is therefore used only to assess spatial representativeness, not to validate channel currents. The tide-gauge comparison supports the modelled sea-level response and its timing at one station, but does not constitute direct validation of local current velocities. The reported trajectories are current-driven passive-particle diagnostics; wave–current coupling, Stokes drift, and material-specific fate processes are not represented. Full article

Marine remote sensing combines broad-coverage satellite observations of the ocean surface with sparse in-situ and underwater observations. However, reconstructing continuous three-dimensional environmental fields remains challenging because of irregular sampling, vertical non-stationarity, and bathymetric barriers. In this paper, a multi-granularity graph collaborative neural network (MG-GCNN) is proposed for high-fidelity field reconstruction and situational awareness in sparse monitoring scenarios. The network abstracts discrete observation points as heterogeneous nodes in the topological graph structure. By incorporating high-resolution bathymetry as a geometric prior, terrain-aware graph construction, vertical feature integration, multi-granularity aggregation, and self-supervised masked node reconstruction are jointly used to capture spatial and vertical dependencies under limited observation availability. Experimental results show that MG-GCNN significantly outperforms baseline interpolation and convolution models in terms of reconstruction accuracy, especially in regions with complex underwater terrain and extreme sampling sparsity. The reconstructed environmental fields can potentially provide three-dimensional environmental inputs for subsequent ocean-acoustic propagation modeling, underwater sensing, and related marine applications. Full article

Robot Operating System (ROS) applications are growing increasingly complex and distributed, incorporating multi-node architectures, real-time communication, and compute offloading to cloud or edge resources. Despite advances in containerization, large-scale robotics deployments often rely on ad hoc provisioning, updating, and scalability strategies. This paper’s key contribution is a module extension for an existing ROS-focused container framework that leverages Kubernetes’ capabilities—controlled rolling updates, ROS-adapted readiness checks, distributed workload management, and embedded observability—to bridge cloud-native practices with robotics development. The result is a unified tool that streamlines the entire ROS DevOps cycle. Experimental validation demonstrates stable operation with minimal message loss across diverse ROS communication patterns, including tests on an industrial mobile manipulator system. By simplifying orchestration and monitoring, this approach enables roboticists to concentrate on application logic rather than deployment and networking complexities. Full article

Critically ill patients frequently move between the intensive care unit (ICU) and the operating room while remaining dependent on ventilatory, hemodynamic, antimicrobial, sedative, analgesic, and renal support. Although extensive perioperative data are generated, clinically relevant information may remain fragmented across devices and electronic systems. This narrative review synthesizes the literature published primarily between 2016 and 2026 on perioperative digital continuity, structured ICU–operating room handoff, interoperability, artificial intelligence (AI)-supported decision support, continuous renal replacement therapy (CRRT)-related pharmacokinetic personalization, and digital twin concepts. Based on this synthesis, we develop a conceptual framework in which the Intelligent Operating Room and Smart ICU function as connected clinical information nodes. The framework is organized around a minimum perioperative continuity dataset, a perioperative delta view, and the author-proposed concept of Time-to-Truth at ICU readmission. Current evidence supports structured handoff and selected AI-assisted physiological prediction, but evidence for improvement in major patient-centered outcomes remains limited and heterogeneous. CRRT illustrates the importance of preserving information on delivered therapy, interruptions, residual kidney function, and treatment-related changes when interpreting drug exposure. The proposed framework is not clinically validated and is intended as a basis for future implementation and prospective evaluation. Overall, the review suggests that improving digital continuity should precede more complex forms of automation in perioperative critical care. Full article

Blockchain consensus still forces a hard choice among scalability, security, and energy use. Redbelly, a leaderless Byzantine Fault Tolerance protocol, largely settles the scalability-versus-energy side of that tension, yet it accepts any cryptographically valid transaction without judging whether it is economically fraudulent or whether the node behind it is misbehaving. That blind spot is what we target. We present ML-Redbelly, a formally specified extension that attaches four learning components to the Redbelly pipeline: a LightGBM gradient-boosted fraud classifier, an Isolation Forest behavioural anomaly detector, a tabular Q-Learning agent for adaptive committee selection, and a Paillier-based federated learning aggregator that keeps model updates private. We prove that this layer leaves Redbelly’s safety and liveness intact, give pseudocode and complexity bounds for every component, and measure the system on the IEEE-CIS Fraud Detection benchmark (400,000 transactions) paired with a faithful discrete-event Redbelly simulator parameterised from measured inputs and validated against the published Redbelly deployment. LightGBM reaches an F1 of 0.783 (precision 0.858, recall 0.719, AUROC 0.963), a 34 percent relative F1 gain over the conference-baseline Random Forest at five times the inference speed. The Isolation Forest detector attains recall 0.885 at a false-positive rate of 0.047, and the Q-Learning agent settles into a stable policy within about 200 rounds across normal, bursty, and Byzantine-attack conditions. End to end, the framework sustains 48,844 TPS on 32 validators (mean over 30 seeds), and because the leaderless superblock commits every proposer’s block in parallel, this throughput advantage over leader-based BFT grows with the validator count (5.0 times PBFT and 2.9 times HotStuff at 32 validators). The learning layer costs only about 4 percent in throughput, since the measured ML inference is small next to the geo-distributed consensus round. Per-transaction energy is comparable across BFT protocols, being dominated by signature verification, and is orders of magnitude below proof-of-work chains, which expend energy on mining. One federated update epoch takes 36 s across 10 nodes with 2048-bit Paillier keys and reconstructs gradients with negligible error. All performance figures are emergent outputs of the discrete-event simulation, which reproduces the published Redbelly benchmark to within a conservative factor of about 1.7. Taken together, these results outline a simulation-validated design for making consensus intelligent as well as fast and identify the steps needed toward real-cluster deployment. Full article

This paper presents a comparative reliability analysis of spacecraft onboard control systems based on their structural representation using directed graph models. A modified approach to reliability assessment is proposed, grounded in representing onboard control systems as directed functional graphs and applied to the analysis of spacecraft architectures in terms of structural fault tolerance. The initial functional schemes are simplified by identifying key system elements and the relationships between them. Based on the resulting representations, graph models are constructed, reflecting the structure of control signal propagation. Structural reliability and fault tolerance assessment are performed using an analysis of graph topological characteristics, including connectivity, graph centrality metrics, the presence of alternative paths, and identification of critical nodes. For systems with a comparable number of elements, differences in structural connectivity significantly affect system resilience to failures. It is shown that a higher degree of connectivity and the presence of redundant control pathways enhance the fault tolerance of the onboard control system. The obtained results confirm the effectiveness of graph-based models for structural reliability and robustness analysis of complex technical systems and can be applied in the design of spacecraft onboard control systems. The proposed approach focuses on the structural and topological properties of the system architecture and does not include probabilistic failure modeling. Full article

A fractional graph-evolution model is formulated on a surface-derived thoracic-aortic branch tree and evaluated through multiresolution geometry and controlled joint-parameter experiments. A checksum-tracked source audit separates three model-specific MRI collections from a shared nominal wall surface and confirms that no validated MRI–STL transformation is available. The surface pipeline yields five terminal openings, three junctions, seven semantic branches, refined cross-sections, and an unapproved same-source candidate. Consequently, the instantiated operator is dimensionless and geometry normalized, rather than a calibrated pressure–flow operator. Graphs with 50, 100, 200, and 400 nodes preserve topology; relative to the internal 400-node discretization, the 200-node graph has a 9.20% 95th-percentile discrepancy in the first 12 positive eigenvalues and a 7.58° maximum principal angle for the first 10 modal subspaces. The analysis establishes a Caputo-consistent control-volume reduction, finite-horizon well-posedness for bounded forcing, non-normal augmented dynamics, observation leakage, finite-band phase conditions, and residual power-law stability. In 810 off-grid synthetic experiments, seven parameters are estimated jointly with repeated noise, multistart optimization, profile likelihood, and held-out testing. The median fractional-order error is 0.001304 and the median held-out complex NRMSE is 0.01086. The results support controlled synthetic practical identifiability on a shared nominal anatomy, not measured hemodynamic calibration or physiological-memory identification. Full article

Complex systems of interacting components often can be modeled by a graph that consists of a set of n nodes and a set of m edges. Such a graph can be represented by an adjacency matrix $A\in {\mathbb{R}}^{n\times n}$, whose $\left(ij\right)$th entry is one if there is an edge pointing from node i to node j, and is zero otherwise. The matrix A and its low-order powers reveal important properties of the graph and allow the enumeration of short paths and cycles that are important for determining short-range communication in the graph as well as node clustering. Closed-form expressions for path matrices of length up to four are derived, and a novel indicator of the structural propensity of the graph to form clusters is proposed. Numerical examples illustrate our analysis. Full article

Unmanned Underwater Vehicle (UUV) swarms operating in complex marine environments must accurately assess threats from surrounding targets to ensure mission success and navigational safety. However, existing threat assessment methods face three fundamental bottlenecks when applied to underwater swarms: the inability to track temporally evolving target intentions, reliance on subjective indicator weighting for multi-target ranking, and vulnerability to spatially heterogeneous sonar noise. This paper proposes a hierarchical threat assessment framework that addresses these bottlenecks through three integrated modules. First, a Dynamic Bayesian Network with a specially designed heading factor tracks target intention over time, propagating threat probabilities across sequential observations and enabling early warning before the closest point of approach. Second, a Vieta’s theorem-based algebraic ranking algorithm constructs comprehensive threat vectors via elementary symmetric polynomials of six indicator utilities, avoiding explicit expert-defined weighting coefficients in the multi-attribute ranking stage while capturing both independent and synergistic indicator interactions. Third, a distance-weighted swarm aggregation strategy suppresses individual sonar noise by assigning higher fusion weights to geographically closer nodes, exploiting the spatial diversity inherent in swarm configurations. Simulation experiments under representative target-motion scenarios validate the framework across four complementary experimental studies. Results demonstrate that the DBN reduces output variance by over 56% compared to static Bayesian networks and responds to abrupt intention changes within 15 s. The algebraic ranking algorithm achieves identical prioritization to TOPSIS without requiring any manual or data-dependent weights. The distance-weighted aggregation reduces root mean square error by 63.2% and improves signal-to-noise ratio by 8.7 dB over equal-weight averaging. The proposed framework provides a principled and interpretable solution for simulation-based autonomous threat perception in representative underwater swarm scenarios. Full article