Search Results (628)

This paper presents a flexible and efficient design method for optimizing the mooring systems of floating structures. Mooring system optimization is challenging because of the strong nonlinearity of mooring system behavior and the many technical constraints that must be satisfied. Furthermore, different mooring configurations can have very different design spaces. While some successful examples of mooring design optimization exist in the literature, developing an optimization approach that can work across various mooring design problems is a larger challenge. We present such a method based on a flexible parameterization that allows a wide variety of mooring designs to be described by a list of variables, a quasi-static mooring model that provides efficient evaluation of a mooring design without directly considering mooring system dynamics, and an optimization framework that generates, evaluates, and adjusts the mooring design while considering user-specified constraints such as offset limits, strength safety factors, and seabed contact limits. We demonstrate the design optimization framework on four mooring design problems, each for a different type of mooring system. We compare the use of different design modes to simplify the optimization problem, showing that they can reduce the computation time by up to 75%. We also compare different optimization algorithms and find that the resulting computational speed can vary by up to 51 times. We perform a sensitivity study on one design and find that the local sensitivity of anchoring radius to water depth has a positive correlation of 0.29, but the global sensitivity shows large nonlinearities. Lastly, we perform a coupled dynamic analysis on one of the optimized designs and find that the predicted mean platform motions and mooring line tensions are within 1% of dynamic results and the extreme motions and tensions are within 14%. Lastly, we show that a DEA-Chain-Polyester mooring configuration is cost-optimal for the given design problem of the demonstrations, which aligns with general industry practice. Full article

Reinforced concrete (RC) slabs in buildings and protective structures are vulnerable to repeated low-velocity impacts caused by falling objects, vehicle collisions, industrial accidents, and successive debris strikes. Such repeated impacts can result in cumulative damage, progressive stiffness degradation, and eventual structural failure. Although externally bonded carbon fiber-reinforced polymer (CFRP) sheets have been widely adopted to improve the impact resistance of RC members, their effectiveness under repeated impact loading is often limited by premature debonding, while the contribution of mechanical anchorage systems to mitigating debonding and improving structural performance remains insufficiently understood. Accordingly, this study experimentally and numerically investigates the repeated low-velocity impact behavior of RC two-way slabs strengthened with externally bonded CFRP sheets incorporating boundary and distributed mechanical anchorage configurations. Four slab groups were investigated: unstrengthened control slabs (SL1), CFRP-strengthened slabs (SL2), CFRP-strengthened slabs with boundary anchors only (SL3), and CFRP-strengthened slabs with distributed anchors across the entire slab area (SL4). Repeated impact tests were conducted using a 92 kg drop weight released from progressively increasing heights until failure. The outcomes showed that strengthening of RC slab with CFRP sheets significantly improved the impact resistance at a 1.50 m drop height by reducing the residual displacement, crater diameter, and indentation depth by up to 67%, 55%, and 70%, respectively, compared with the control slabs. The incorporation of mechanical anchors further delayed premature CFRP debonding, maintained the CFRP–concrete bond, and enhanced the structural response, achieving maximum reductions of 77%, 63%, and 85%, respectively. Furthermore, the anchored slabs withstood repeated impacts from a 2.50 m drop height, whereas both the control and unanchored CFRP-strengthened slabs failed at a 2 m drop height. The developed finite element model accurately captured the structural response, CFRP debonding, anchorage failure, and damage evolution of the RC slabs, with good agreement between the numerical predictions and the experimental observations in terms of failure patterns, damage characteristics, and residual displacements. The proposed strengthening strategy and validated numerical model provide a reliable framework for assessing the effectiveness of different mechanical anchorage configurations and predicting the progressive failure behavior of CFRP-strengthened RC slabs subjected to repeated low-velocity impacts. Full article

Under complex terrains and extreme environmental conditions such as high wind speeds, prestressed rock anchor foundations for onshore wind turbines are subjected to multiple coupled adverse effects during long-term service, including cyclic wind loading, temperature variation, groundwater intrusion, and rock mass weathering. These factors significantly affect structural performance and service life through corrosion and material degradation processes, while conventional design methods mainly focus on ultimate bearing capacity and lack a systematic consideration of corrosion-induced deterioration mechanisms and long-term performance evolution. Without changing the theoretical framework of current design codes, this study introduces a durability-oriented design concept and explicitly incorporates corrosion effects and material degradation into the analytical system of prestressed rock anchor foundations. First, from the perspective of anchor force evolution, a time-dependent analysis method for long-term prestress loss is established, considering the coupled effects of steel corrosion, material relaxation, and cyclic loading. Second, for the mechanical behavior of group anchor systems, a shear capacity model is proposed that accounts for rock mass strength degradation and grout–rock interface deterioration. Meanwhile, the coupling relationship between foundation void development and groundwater seepage is analyzed, revealing its critical role in the corrosion evolution process. On this basis, a coordinated design method for foundation dimensions and prestress parameters is developed to satisfy both load-bearing capacity and durability requirements. Finite element analysis is further conducted to verify the stress and deformation characteristics of the foundation–rock–anchor system under nonlinear conditions. Engineering case studies demonstrate that the proposed method not only meets bearing capacity requirements, but also effectively suppresses void development, reduces corrosion risk, delays structural performance degradation, and improves long-term service reliability. The findings provide a theoretical basis and engineering reference for the durability design and lifecycle performance optimization of prestressed rock anchor foundations for onshore wind power structures in extreme environments. Furthermore, the study underscores the critical role of advanced anti-corrosion coatings and surface protection systems in mitigating the coupled corrosion-degradation mechanisms, aligning with the scope of this Special Issue on corrosion protection and durability of infrastructure in harsh environments. Full article

To elucidate the active–passive reinforcement mechanisms of rock bolts and the evolution of shear strength in anchored rock joints, this study integrates theoretical analysis, laboratory direct shear tests, and numerical simulations to investigate the deformation and failure characteristics of fully grouted, end-anchored, and prestressed bolted specimens. The results show that bolt reinforcement can be classified into prestress-dominated active action and dislocation-induced passive action. The shear strength curve of anchored rock joints exhibits four distinct stages with increasing shear displacement: initial slip, elasticity, yielding, and softening. Fully grouted bolts fail primarily by tensile–shear fracture, enabling a rapid increase in shear strength at small displacements. In contrast, end-anchored bolts undergo S-shaped bending and form symmetrical plastic hinges on both sides of the joint, sustaining resistance under large displacements albeit with lower peak strength. While the laboratory tests experimentally clarified the distinct failure modes and passive shear resistance mechanisms of fully grouted and end-anchored bolts, the quantitative partitioning between active and passive contributions was derived from a numerically simulated prestressed bolt model. The simulations indicate that for prestressed bolts, the active contribution accounts for approximately 69.6% of the total shear strength enhancement, while the passive contribution is about 30.4%. These findings yield actionable design criteria: end-anchored or yielding bolts are recommended for high-geostress environments or scenarios involving large potential deformations to exploit the large-deformation bearing capacity of passive action; conversely, prestressed bolts should be prioritized where strict control of early-stage deformation is required to maximize active support efficiency. Full article

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

Trajectory Representations Learning (TRL) serves as a foundational technology for supporting intelligent transportation. However, models trained on real-world data often suffer from performance degradation caused by inherent spatiotemporal distribution bias, which reflects the heterogeneity of urban structures and human movement behaviors. This leads to representations that overfit to frequent patterns, resulting in weak robustness and limited generalization to sparse or atypical trajectories. To address these issues, this paper presents a novel perspective, anchor-guided balanced learning, and instantiates it with a framework, AnchorTRL. AnchorTRL introduces anchors to proactively construct a balanced semantic space instead of passively fitting the empirical data distribution. Specifically, AnchorTRL designs a spatiotemporal anchor identification algorithm to recognize trajectory anchors that comprehensively cover the data manifold. And, it proposes a calculation method to measure all trajectories’ semantic similarity with anchors. Additionally, it develops an anchor-based balanced sampling strategy to mitigate the dominance of frequent patterns and steer the model towards learning a more balanced representation. Finally, it constructs a multi-task contrastive learning objective with adaptive constraints to enhance the aggregation of semantically similar trajectories. Experimental results show that AnchorTRL outperforms existing baseline methods in tasks such as travel time estimation and similar trajectory queries, demonstrating its effectiveness and robustness. This research provides methodological support for constructing more reliable trajectory representation learning models, and offers new insights for optimizing intelligent transportation applications under spatiotemporal biases. Full article

Background: This study examines how four canonical behavioral biases (overconfidence, herding, anchoring, and loss aversion) influence investor decision-making in the Saudi stock market (Tadawul), and whether overconfidence and loss aversion operate as moderating forces on herding and anchoring, respectively. Methods: Employing a quantitative, cross-sectional design with a stratified sample of 384 retail investors, the study applies Partial Least Squares Structural Equation Modelling (PLS-SEM) to test six hypotheses derived from Modern Portfolio Theory and Behavioral Finance frameworks. The measurement model satisfies established thresholds for reliability, convergent validity, and discriminant validity. Results: Results confirm that loss aversion is the dominant predictor of behaviorally influenced decision-making (β = 0.402, p < 0.001, f² = 0.188), followed by herding (β = 0.234, p < 0.001) and overconfidence (β = 0.164, p = 0.001), while anchoring does not exert a statistically significant independent effect (β = 0.102, p = 0.084 one-tailed, p = 0.168 two-tailed). Neither the overconfidence × herding (β = 0.005, p = 0.920, two-tailed) nor the loss aversion × anchoring (β = −0.039, p = 0.330, two-tailed) interaction terms reach significance, indicating that these bias pairs operate as independent additive forces rather than compounding systems. The model explains 55.7% of the variance in investor decision-making (R² = 0.557). Conclusion: The findings advance behavioral finance theory in GCC and Islamic equity markets by (1) demonstrating non-equivalence of anchoring effects relative to Western-market benchmarks, (2) resolving competing theoretical predictions about bias interaction effects, and (3) providing context-specific evidence that loss aversion subsumes anchoring cognition in the Saudi market. Practical implications for the Capital Market Authority, financial educators, and individual investors are discussed and contextualized within the Saudi market setting. Full article

To clarify the bearing characteristics and load transfer mechanisms of long large-diameter pile foundations in dense silty fine sand strata within the middle and lower reaches of the Yellow River, a graded tension–compression anchor reaction loading test method was devised and implemented using a field-configured apparatus. This approach enables graded static load testing on large-tonnage long bored cast-in-place piles. Then, the relative displacement and settlement between pile and soil under vertical cyclic loading were analyzed. Finally, numerical simulations were adopted to study the settlement behavior of pile tops and ends under cyclic loads representative of beam yard operational conditions (20 cycles). Results indicate that, under vertical loading, the shaft friction resistance and tip resistance of large-diameter long bored cast-in-place piles are not mobilized simultaneously, but sequentially. The degree of shaft friction is related to the magnitude of pile top loading, soil properties, burial depth, and construction methods. The soil between piles generates vertical resistance to horizontal force-transfer rods, becoming part of the pile foundation’s bearing capacity and sharing the load. Moreover, in dense silty fine sand strata, long large-diameter pile foundations exhibit pure friction pile behavior. When calculating the bearing capacity of such piles, parameters from geotechnical reports based on code-specified values should be multiplied with corresponding correction coefficients. In addition, the shaft friction resistance in dense silty fine sand layers remains under-mobilized. Limited loading–unloading cycles in permanent–temporary integrated beam yard operations do not induce significant deformation in pile foundations, indicating minimal impact on their bearing performance. Full article

This study investigates the structural impact of marine conservation dimensions and institutional frameworks on visitor experiences and behavioral intentions within the National Marine Park of Alonissos–Northern Sporades (NMPA). Anchored in the sustainability prism model (SPM), the research examines how conservation-driven environmental capital, alongside economic, socio-cultural, and institutional pillars, influences tourist satisfaction and destination loyalty. Primary quantitative data were garnered via a cross-sectional survey administered to a statistically robust sample of 2251 visitors. Structural equation modeling (SEM) reveals that all four SPM dimensions significantly influence both immediate satisfaction and long-term loyalty. Crucially, the empirical outputs unveil an asymmetric dualism: while immediate tourist satisfaction is predominantly driven by the sensory consumption of pristine biodiversity and active marine conservation (β = 0.577), long-term loyalty is fundamentally anchored in the perceived efficiency of the institutional conservation governance framework (β = 0.413). These findings underscore the imperative of integrating transparent institutional mechanisms with active ecosystem preservation to secure the socio-ecological viability of marine conservation areas. Ultimately, this study provides actionable insights for balancing escalating ecotourism demands with strict, zero-compromise biodiversity conservation in vulnerable marine sanctuaries. Full article

Curling is a strategic sport in which shot decisions involve both expected rewards and inherent risks; expected-score distributions (ESDs)—probability distributions over possible final scores—capture this uncertainty as a risk-aware strategic indicator. Although unified frameworks predicting ESDs across all shots have been proposed, their internal behavior and learned output characteristics have not been systematically examined. We construct an all-shot ESD prediction framework grounded entirely in real-match board configurations drawn from World Curling Federation championship events and conduct a shot-wise analysis along two complementary axes: accuracy, the model’s reproduction of its training targets, and plausibility, the validity of those targets at aggregate and in-match scales. Accuracy degrades monotonically with shot number; only shot 16 admits comparison with an independent reference, while intermediate-shot labels are bootstrap rollouts of the next-shot model. An independent ground-truth probe at the first backward step (shot 15,500 boards × 95 contexts) bounds the consequence of this bootstrap structure: the resulting deviation is a small selection-induced hammer-underestimation bias common to both FiLM and concatenation chains, whose magnitude is more than an order of magnitude larger than the FiLM chain versus concatenation chain stage difference, and full multi-step verification beyond the first step is structurally out of reach. Within this scope the aggregate ESD shares the gross shape of the empirical end-score distribution and the qualitative hammer/non-hammer ordering, with a hammer-favorable offset attributed—after disentangling intent from execution on the shot-percentage 100% subset—to a label execution-noise envelope that is tighter than the realized play of top-tier matches. Within real matches the chain responds smoothly to each delivered stone and, at the directly validated terminal shot, assigns mean probability $\approx 0.71$–$0.72$ to the realized end score under intended execution (against $0.28$ for a marginal-frequency baseline), transferring from senior to junior populations. The framework is best read as an internally coherent chain anchored to a directly validated final-shot calibration, statistically plausible under intended execution with a clear boundary at execution failure. Two structural limitations remain: rare high-magnitude outcomes are scarce in real data and produce a heavy upper tail of accuracy errors, and a single fixed execution-noise envelope that is tighter than top-tier realized play accounts for the aggregate hammer-side offset and motivates recalibration against real-match execution statistics. Full article

Digital forensics increasingly operates under extreme data growth, where exhaustive bit-wise imaging of modern storage is constrained by time, storage, and processing cost. Selective imaging and artifact prioritization methods reduce acquisition volume, but they typically order artifacts by content, location, or offense type rather than by the relationships that make evidence interpretable. As a result, they risk discarding the contextual artifacts on which attribution and corroboration depend. This paper introduces the High-Value Evidence Area (HVEA) pyramid, a dependency-oriented abstraction that organizes Windows forensic artifacts into ten operational layers (A–J) grouped into four evidentiary tiers: attribution anchor, primary payload, behavioral contextualization, and structural corroboration, in which acquisition order follows interpretive prerequisites rather than artifact salience. The model is evaluated on nine Windows forensic images spanning Windows XP, Vista, and Windows 11, combining retrospective analysis of the M57-Patents corpus with a controlled fourteen-day behavioral experiment. Across systems and operating system generations, HVEA layers exhibit stable evidentiary function despite changing artifact implementations; behavioral execution telemetry persists even where user content is sparse or deliberately concealed; and a three-source timestamp corroboration pattern consistently converges within thirty seconds across independent OS mechanisms, providing an empirically grounded defensibility threshold for event reconstruction. The results support a dependency-ordered, NIJ-aligned selective acquisition strategy that preserves interpretive context while reducing acquisition footprint by approximately 95% (a 15–20× reduction) on the controlled Windows 11 image. The present evaluation is scoped to Windows environments; extending the tier mappings to other platforms is identified as future work. Full article

This study examines how different prompt anchoring strategies influence the conceptual representation of LLM-generated keywords and compares those effects with the effects of model selection. A controlled exploratory experiment evaluated four prompt conditions—No Examples, Brief Keywords, Detailed Explanations, and Author-Based Examples—across T. D. Wilson’s four information behavior dimensions using 1068 abstracts. Four LLMs (GPT-4o-mini, Claude-3-haiku, Gemini-2.0-flash-lite, and DeepSeek V3) were evaluated under all prompt conditions, yielding 17,036 valid observations. Results indicate that model identity accounts for substantially more variance in keyword generation (η² = 0.309) than prompt condition (η² = 0.069), although these estimates should be interpreted with caution given the repeated-measures design and assumption violations. Prompt anchoring, however, consistently reconfigured the conceptual distribution of outputs across all models, indicating that it influences conceptual representation even when model effects are larger. Author-Based Examples substantially increased representation of the typically underrepresented Information Sharing dimension, whereas Detailed Explanations produced the highest overall generation rates and the broadest dimensional coverage. These findings further indicate that different anchoring strategies involve consistent trade-offs in dimensional coverage. The study thereby identifies prompt anchoring as a source of methodological variation in LLM-assisted content analysis, indicating that anchoring strategies should be explicitly specified, justified, and reported as part of the study methodology. Full article

Spatial updating—the ability to continuously revise spatial representations during locomotion—is fundamental to adaptive navigation and depends on flexible reference frames. Although previous research has established independent effects of field cognitive style and scene layout on spatial performance, their interaction and underlying neural substrates remain poorly understood. The present study examined how field dependence–independence and environment geometry jointly modulate spatial updating by combing the judgment of relative direction (JRD) paradigm with functional near-infrared spectroscopy (fNIRS). Forty participants were recruited and assigned to two groups (20 field-independent [FI] and 20 field-dependent [FD]) based on Embedded Figures Test scores. They completed directional pointing tasks in two virtual environments: a geometrically structured rectangular spaces affording explicit orthogonal reference axes and an ambiguous oval environments devoid of stable global geometric anchors. Behaviorally, FI individuals exhibited shorter response time in rectangular layouts yet superior accuracy in oval layouts relative to FD individuals. Neurally, the middle frontal gyrus (MFG) emerged as a critical locus exhibiting a significant interaction effect between cognitive style and environmental layout. Significant main effects of field cognitive style were observed in the precentral gyrus, superior parietal lobule, and paracentral lobule, with FI individuals showing greater oxyhemoglobin (HbO) elevation than FD participants. Collectively, these findings may tentatively suggest an interpretation that FI individuals flexibly alternate between internal egocentric and external allocentric reference frames during spatial information processing, whereas FD individuals predominantly rely on inherent structural cues embedded in the external environment. These findings may reflect cortical hemodynamic correlates of field cognitive style differences during spatial processing, and may offer empirical references for relevant cognitive neuroscience research and subsequent exploratory applications. Full article

Anchoring systems for deep-water floating structures must withstand complex, hydro-mechanical (H-M) coupled uplift forces throughout an operational life extending over decades, yet fundamental understanding of this complicated H-M behavior remains insufficient. This paper presents a comprehensive review of H-M coupled foundation–seabed interactions under uplift loading. Key experimental findings are synthesized to demonstrate that the fundamental distinction between uplift and compression lies in the foundation-soil interface. Unique interfacial uplift behaviors are highlighted, including the progressive formation of an interfacial gap and the evolution of transient suction within it. This water-filled gap enables sustained tensile contact stress post-detachment, transforming traditional soil-structure interaction to the more general “soil–interfacial fluid–structure” interaction framework. A 1D conceptual model, representing a mechanistic extension of Terzaghi’s consolidation theory, is discussed to further elucidate these H-M mechanisms. For complex 3D numerical simulations, the limitations of traditional total-stress interface models are discussed, and specialized H-M thin-layer and zero-thickness interfaces designed for uplift modeling are critically examined regarding their advantages and limitations. The review concludes by outlining a roadmap for the next research frontier: high-fidelity treatments of sustained multidirectional cyclic loading, suction-induced liquefaction, and the long-term rheological evolution of the interface. Full article

To address identity cloning, credential leakage, and delayed authorization in unmanned aerial vehicle (UAV) systems operating in open airspace, we propose a zero-trust authentication and authorization framework. SM9 identity-based mutual authentication establishes a traceable digital identity anchor. On-demand rotor acoustic verification then checks whether the current platform matches the registered identity. A Beta-distribution trust model converts authentication, verification, and behavioral evidence into dynamic authorization decisions. Experimental results show that the acoustic module blocks 104 of 110 spoofing samples. Under identity-cloning attacks, the attacker’s trust value decreases from about 0.97 to 0.27 and falls below the isolation threshold of 0.4. Compared with continuous acoustic authentication, on-demand triggering reduces authentication activation frequency, average inference latency, and normalized energy consumption by 86.8%, 83.6%, and 83.0%, respectively. These results indicate that the framework links identity confirmation, entity verification, and continuous authorization under resource-constrained UAV conditions. Full article