Search Results (224)

Utilizing underwater vehicles for hydropower infrastructure inspection is increasingly vital. However, these GNSS-denied and confined environments pose significant navigation challenges: Inertial Navigation Systems (INSs) suffer cumulative drift, Doppler Velocity Logs (DVLs) face acoustic blind zones near walls, and visual navigation frequently fails in highly turbid waters. To address these issues, this paper proposes a tightly coupled multi-source (INS/acoustic/optical/vision) navigation algorithm leveraging prior wall geometry constraints. Developed within an Error-State Kalman Filter (ESKF) framework, the model seamlessly accommodates sensor spatiotemporal heterogeneity. To overcome optical failures, a structural surface constraint model is innovatively constructed using single-beam sonar ranging. The core contribution involves transforming sonar ranging data into 6-DOF spatial pose constraints based on the dam’s planar characteristics, effectively bounding the localization drift perpendicular to the surface. Field experiments at the hydropower station dam demonstrate that under extreme conditions with total visual failure, the proposed algorithm effectively constrains critical motion degrees of freedom. By maintaining the wall-tracking error within 0.08 m (Root Mean Square Error, RMSE)—which effectively represents the relative localization error given the known absolute position of the structural wall—this method significantly enhances the operational robustness and precision of close-wall inspections in extreme underwater environments. Full article

Underground mine emergencies compromise fixed communication infrastructure exactly when situational awareness is most critical for effective rescue operations. Existing LoRa mesh protocols fail in underground mines because they ignore the structured topology of tunnel networks, specifically the waveguide effect along straight galleries, severe signal discontinuity at junctions, and the dead-end geometry of working faces. This paper presents the Topology-Aware Concurrent LoRa (TACL) mesh protocol, in which each node autonomously infers its structural role from local RF observations and packet header information, without GPS, pre-loaded mine maps, or central coordination. Role classification resolves the contender estimation problem left open in the prior concurrent transmission literature, enabling provably bounded timing offsets before transmission. TACL assigns a spreading factor (SF)12 to dead-end source nodes for maximum link robustness and SF7–SF10 to relay nodes to create the inter-SF orthogonality margin required for concurrent decoding at junction nodes. Monte Carlo simulation of over 2000 trials yields TACL a PDR of 80.5% versus near-zero for all three baselines, confirming that topology-aware SF diversity is the necessary and sufficient mechanism to prevent junction collision collapse. Hardware deployment at the Missouri S&T Experimental Mine yields a 4.0× PDR improvement over the topology-agnostic concurrent transmission (CT)-fixed baseline, a median end-to-end latency of 1815 ms with 84× tighter latency spread than ALOHA-based protocols and 2.5× lower energy per delivered packet. These results establish that explicit exploitation of underground mine topology is essential for reliable, predictable, and energy-efficient emergency mesh communications in post-disaster underground mine scenarios. Full article

This paper presents the INFRARES tool, a fully parameterized, interactive, and freely available tool for the resilience assessment of bridges and tunnels within Greece’s transportation networks, under the impact of single or multiple hazards, including earthquakes and floods. The tool facilitates the application of a comprehensive methodology developed through the INFRARES project: Towards resilient transportation infrastructure in a multi-hazard environment research project. The resilience of each examined asset is quantified for the selected hazard scenario using a resilience index and a corresponding resilience grade. The INFRARES tool introduces two key innovations over previous approaches: first, it incorporates both structural and geotechnical components of bridges, overpasses, and tunnels in the vulnerability assessment step; second, it enables GIS-based visualization of the resilience index across selected single- or multi-hazard scenarios. In this context, INFRARES serves as a proactive decision-support tool, supporting authorities, infrastructure operators, and stakeholders to effectively assess, manage, and mitigate the impacts of diverse hazards on transportation systems, thereby enhancing the safety, reliability, resilience, and sustainability of transportation infrastructure under multi-hazard conditions. The proposed tool and the obtained results may support resilience-informed decision-making, prioritization of mitigation measures, and sustainable management of transportation infrastructure exposed to multiple natural hazards. Full article

Traditional methods for the structural analysis of tunnel linings are often hampered by complexity and significant time consumption, creating the need for a more efficient analysis workflow. To address this issue, this study develops an application oriented surrogate modeling framework centered on parametric design-driven MINN. The framework of PD-driven MINN leverages parametric design to systematically generate a comprehensive dataset of 2000 parameter groups from FEA, providing a robust foundation for model training. Meanwhile, the MINN architecture is tailored to process these diverse inputs, effectively capturing complex parameter interactions while balancing computational speed and modeling accuracy. To validate the proposed strategy, the model’s predictions were rigorously compared against real-world engineering data from pre-buried sensor measurements in an actual transportation tunnel project. The results indicate the reliability of a parametric design-driven MINN surrogate model. A comparative analysis demonstrates its superior fitting performance and convergence over traditional artificial neural networks. This study demonstrates the practical value of adapting existing neural network techniques and integrating them with parametric design to support more efficient tunnel lining structural analysis. Full article

This study develops a quantitative risk assessment framework that explicitly incorporates site-dependent variability in NATM (New Austrian Tunneling Method) tunnel construction projects. The underlying motivation is that identical risk factors can exhibit substantially different risk levels depending on project-specific site conditions. Conventional risk assessment approaches, which rely primarily on probability and impact ratings, are inherently limited in their ability to capture such variations across different project environments. To address this gap, key site condition factors affecting NATM tunnel construction were systematically identified and integrated into the existing risk assessment framework through a structured scoring and weighting process. Eight site condition factors were selected based on an extensive review of domestic and international literature, underground safety evaluation reports, tunnel design standards, geotechnical information databases, standard cost data, and expert consultation. These factors—Geotechnical Condition, Construction Schedule Float, Construction Budget Contingency, Spoil Bank Location, Likelihood of Civil Petitions, Underground Water Level, Environmental (Noise, Vibration), and Site Accessibility (Traffic Constraints)—were each quantified using a five-level scale ranging from 0.6 (very favorable) to 1.4 (very unfavorable). Subsequently, a composite site condition index was derived by combining the assigned scores with corresponding weights, and this index was incorporated as an adjustment coefficient into the conventional risk scoring system. The results demonstrate that, when the composite site condition index is considered, both the final risk magnitude and management priority vary depending on site-specific conditions, even for identical risk factors. This indicates that the proposed framework provides a more refined representation of actual project environments than traditional probability–impact-based approaches. The model can also serve as an effective decision-support tool for developing risk mitigation strategies tailored to specific site characteristics. Accordingly, the proposed model enhances the accuracy of risk assessment in tunnel projects and facilitates the rational identification of critical risks requiring prioritized management. However, because certain evaluation criteria rely on expert judgment, further validation through diverse real-world case studies and improvements to the objectivity of the evaluation framework remain necessary. Full article

Vision-based structural defect detection methods based on YOLOv11 have achieved promising performance in recent years; however, their robustness in real engineering environments remains limited due to illumination variation, shadow occlusion, surface contamination, and complex background textures. Existing data-driven approaches primarily rely on visual appearance features while neglecting the intrinsic geometric continuity and morphological characteristics associated with structural failures such as cracks and spalling. To address these challenges, this study proposes an enhanced defect detection framework termed GCA-YOLO for intelligent structural inspection. The proposed method integrates a Geometric Constraint Attention (GCA) module and a Residual Efficient Channel Attention (RECA) module to improve feature representation. Instead of explicit physical simulation, the GCA module embeds morphology-guided geometric priors into the attention mechanism using differentiable gradient and Laplacian operators. This enforces structural continuity perception and suppresses geometrically inconsistent responses caused by background noise. Furthermore, a geometry confidence gating mechanism adaptively modulates the contribution of morphological features, while the RECA module recalibrates channel-wise responses to enhance the representation of weak and low-contrast defects. To comprehensively evaluate the proposed method, experiments were conducted on three representative datasets, including a public crack dataset and two self-built datasets (one for peeling/detachment and one for crack defects). These datasets were collected from diverse civil infrastructure scenarios such as bridges, tunnels, and pavements under challenging conditions including low illumination, shadow occlusion, complex textures, and heterogeneous backgrounds. Compared with the baseline YOLOv11 model, the proposed GCA-YOLO framework improves mAP@0.5 by 2.2%, 2.5%, and 15.9% on the public crack dataset, the self-built peeling/detaching dataset, and the self-built crack dataset, respectively. Meanwhile, Recall is improved by 4.6%, 3.8%, and 33.1%, respectively, demonstrating the effectiveness of the proposed dual-attention framework in enhancing the completeness of defect localization and reducing missed detections. Despite these performance gains, the proposed framework maintains a lightweight architecture and does not introduce significant computational overhead. Experimental results demonstrate that the proposed framework achieves strong robustness, stable generalization capability, and favorable detection efficiency across different defect categories and engineering scenarios, demonstrating promising potential for intelligent infrastructure inspection, urban safety monitoring, and practical engineering deployment. Full article

Negative capacitance (NC) has been reported across a wide range of physical systems, yet its interpretation has remained fragmented due to the lack of a unified conceptual framework. Existing explanations—spanning ferroelectric free-energy curvature, tunneling transport, plasmonic resonances, and electronic compressibility—have often been treated as unrelated or even contradictory. This review resolves these inconsistencies by showing that all manifestations of NC arise from non-synchronization between external excitation and internal response. We classify NC into three fundamental categories: temporal mismatch, originating from delays or inertia in charge or polarization dynamics; spatial mismatch, caused by nonuniform field or mode distributions; and quantitative mismatch, resulting from intrinsic parameter reversal such as negative curvature or negative compressibility. Despite their diverse physical origins, these mechanisms share the same mathematical signature (${\mathrm{C}}_{eff}=\partial \mathrm{Q}/\partial \mathrm{V}<0$). Organizing NC within this unified framework clarifies long-standing ambiguities, connects previously isolated research fields, and establishes a systematic foundation for engineering NC in electronic, photonic, and quantum devices. The framework further highlights tunnel-current-induced NC as a representative single-particle mechanism within the temporal mismatch category, expanding the scope of NC beyond ferroelectricity and collective modes. Overall, this work positions NC not as a singular anomaly but as a universal response class emerging from the interplay between excitation and internal dynamics. Full article

This study provides a comparative performance evaluation of commercial Precise Point Positioning Real-Time Kinematic (PPP-RTK) and public Network RTK (NRTK) services for vehicle-based positioning in urban and suburban environments. Using low-cost u-blox ZED-F9 receivers, the research assesses the accuracy, availability, and robustness of the u-blox PointPerfect service against a regional NRTK network across diverse real-world scenarios, including high-speed highway conditions and signal-challenging urban corridors. The experimental framework utilizes a rigid-bar setup for high-precision ground-truth validation and incorporates an independent vertical accuracy assessment against a LiDAR-derived digital elevation model (DEM). The results demonstrate that all tested configurations achieve decimeter-level accuracy. Notably, the integration of PPP-RTK with an inertial measurement unit (IMU) delivers performance nearly equivalent to NRTK, effectively mitigating vertical biases and ensuring positioning continuity in GNSS-denied areas such as tunnels. These results confirm that low-cost GNSS solutions, when paired with modern augmentation services and IMU integration, can meet the stringent demands of mass-market applications like Cooperative Intelligent Transport Systems (C-ITS) and autonomous mobility. Full article

This study presents the aerodynamic design of the wing system for a fixed-wing vertical take-off and landing (VTOL) unmanned aerial vehicle (UAV), developed to enhance energy efficiency and operational performance in agricultural applications. The design responds to the limitations of conventional multirotor drones, which are limited by low endurance and high energy consumption, and crop-dusting aircraft, which are unsuitable for irregular terrain such as that found in Chihuahua, Mexico. A comprehensive methodology was adopted, integrating the selection of airfoils optimized for low-Reynolds-number conditions, computational fluid dynamics (CFD) simulations, winglet incorporation, and experimental validation through wind tunnel testing. The SELIG 1223 airfoil was selected for its superior aerodynamic efficiency, demonstrating a potential reduction of up to 55% in power requirements compared to multirotor configurations. Despite some variability in experimental results, the proposed design demonstrated consistent feasibility and reliability. Future work will focus on field validation and geometric adaptation to diverse operational scenarios, reinforcing its applicability across heterogeneous agricultural landscapes. Full article

Accurate prediction of aggregation in suspensions is crucial for diverse engineering applications. This paper develops a sequential theoretical strategy, based on tunnel theory, to predict the aggregation configuration in magnetic compound fluids (MCF) by evaluating their volume concentration C_v. We formulated the viscosity η, resistance R, and capacitance C resulting from aggregation as functions of C_v. This involved a theoretical procedure using tunnel theory, refined using experimental data, including vertical force F_v arising from the concentration gradient, as well as electrical conductivity σ and permittivity ε. The theoretical formulation for η was further refined by considering hypothetical aggregation configurations, specifically non-uniform particle distribution and agglomerations approximated as spheroids with axis ratio κ, along with experimental data on shear flow. For R and C, the formulations were refined using experimental data for σ and ε, together with the relationship between C_v and the applied magnetic field H_v derived from tunnel theory and F_v. This sequential theoretical analysis yielded final formulations for η, R, and C as functions of H_v and initial volume concentration C_v,o. Specifically, η was expressed as a function of κ and C_v,o for the shear and stress–shear strain γ’ relationship under conditions of H_v < 200 mT, 11 < C_v,o < 30 vol.%, and γ’ < 300 1/s. R and C were determined under conditions of H_v < 150 mT and 11 < C_v,o < 30 vol.%. These findings pave the way for novel theoretical predictions of C_v, R, and C based solely on H_v data, a capability crucial for designing diverse materials. Full article

Hidradenitis suppurativa (HS) is a chronic inflammatory dermatosis characterized by recurrent nodules, abscesses, and sinus tract formation in intertriginous skin. Although HS is increasingly recognized as an autoinflammatory condition rather than a classical infection, antimicrobial therapies remain central to disease management, implicating a potential role for the cutaneous microbiome in disease activity. Recent advances in culture-independent sequencing techniques have enabled more detailed characterization of microbial communities in HS, revealing consistent alterations in microbial composition and diversity. Compared with healthy skin, HS lesions exhibit reduced microbial diversity, depletion of commensal organisms such as Cutibacterium acnes, and enrichment of anaerobic bacteria including Prevotella, Porphyromonas, and Finegoldia. These alterations are more pronounced in chronic, tunnel-forming disease and are frequently associated with biofilm formation, which may contribute to treatment resistance and persistent inflammation. Microbiome changes have also been observed beyond overtly lesional skin, suggesting a broader field effect. Evidence regarding extracutaneous microbial compartments, particularly the gut microbiome, remains limited and heterogeneous, while methodological variability in sampling, sequencing, and treatment exposure continues to complicate cross-study comparisons. Emerging data further suggest that immune-targeted therapies, including biologic and small-molecule agents, may indirectly influence microbial community structure through modulation of the inflammatory milieu. Collectively, the available evidence supports cutaneous dysbiosis as a characteristic feature of HS that may potentially interact bidirectionally with immune dysfunction. Future longitudinal, multi-omic studies integrated with clinical phenotyping will be critical to clarify causal relationships and to determine whether microbiome modulation can be leveraged to improve therapeutic outcomes in HS. Full article

Sustainable vegetable production requires strategies that optimize yield while conserving water and minimizing resource inputs. This study, conducted at the Horticulture Research Farm near Absaraka, ND, evaluated the performance of several paste-type tomato (Solanum lycopersicum) cultivars under different irrigation strategies in high-tunnel and open-field production systems to identify cultivar and irrigation combinations that support sustainable production. Across seasons and production environments, cultivar significantly influenced marketable yield, fruit number, fruit size, and the proportion of unmarketable fruit, whereas irrigation treatments had limited effects on total and marketable yield. High-yielding cultivars such as ‘Granadero’, ‘Pozzano’, ‘Cauralina’, and ‘Amish Paste’ consistently produced greater marketable yields in both production systems, although ‘Cauralina’ also exhibited higher levels of fruit cracking and unmarketable yield. In high-tunnel production, deficit irrigation strategies based on soil moisture thresholds (10% and 30% management allowable depletion) maintained yields comparable to time-based irrigation, suggesting that water-efficient irrigation scheduling can sustain productivity. In the open field, cultivar responses varied under different irrigation regimes, highlighting the importance of selecting cultivars adapted to water-limited conditions. Fruit quality attributes, including soluble solids content and titratable acidity, were primarily influenced by cultivar rather than irrigation. Overall, the findings demonstrate that cultivar selection combined with water-efficient irrigation management can maintain tomato productivity while reducing water use and production losses. These results support the development of more sustainable tomato production systems that enhance resource-use efficiency, reduce waste from unmarketable fruit, and maintain fruit quality across diverse production environments. Full article

Airfoil flow-field prediction is important for aerodynamic design, but wind-tunnel testing and computational fluid dynamics (CFD) remain costly and time-consuming. Deep learning enables fast inference, yet many existing models still rely on fixed grid representations, which may lead to insufficient learning in high-gradient regions and larger local errors. This study proposes Spatial Multilayer Perceptron (Spatial MLP) together with an Error-Gradient-Guided Data Sampling (EGDS) strategy for airfoil flow-field prediction. Spatial MLP adopts a coordinate-based point-wise prediction framework. A spatial decoder is introduced as an auxiliary branch to enhance global flow consistency during pretraining, while channel-wise multi-head attention is incorporated to improve cross-variable feature coupling. EGDS prioritizes physically informative points according to relative prediction error and gradient magnitude, while retaining random samples to preserve data diversity. Experiments on an independent test set show that Spatial MLP reduces the mean relative error (averaged over the velocity components u, v, and pressure p) by 15.2% relative to the MLP baseline. With EGDS, the overall mean relative error is further reduced by 34.5% relative to the MLP baseline. These results demonstrate that combining global consistency constraints with targeted sampling effectively improves both global prediction accuracy and local reconstruction quality in high-gradient flow regions. Full article

Water inrush can easily occur during the construction of diversion tunnels crossing water-rich faults, and large-scale water inrushes pose a great threat to construction personnel and machinery. For the construction safety of the diversion tunnel, it is very important to accurately predict the risk of water inrush. Therefore, to reduce the occurrence of water inrush disasters in tunnels, this paper establishes a diversion tunnel water inrush risk prediction model based on the NRBO-XGBoost algorithm on the basis of giving full play to the value of engineering data. Nine indicators were selected from engineering geological conditions, hydrogeological conditions, and tunnel construction conditions on the basis of fully mining engineering data, and the prediction indicator system of the water inrush risk of tunnels through water-rich faults was established. The model was trained and tested using 120 valid samples collected from the Longjinxi diversion tunnel, which realizes accurate and fast water inrush risk prediction in the construction process. Its predictive performance was compared with that of BPNN and the standard XGBoost model. The R² and MAE of the novel method are 0.9129 and 0.0667, respectively, which are both superior to those of other methods. It confirms the proposed model’s reliability and effectiveness. Full article

Dark current represents a fundamental limiting factor in photodiode performance, establishing the noise floor and constraining detectivity in low-light applications. This comprehensive literature review examines publications covering the physical mechanisms underlying dark current generation and diverse techniques employed for its reduction. Covered mechanisms include diffusion current, Shockley–Read–Hall (SRH) generation–recombination, trap-assisted tunneling, band-to-band tunneling, and surface leakage, each examined with respect to its physical origin and characteristic signatures. Reduction strategies are categorized into thermal management approaches, surface passivation techniques including atomic-layer-deposited aluminum oxide (ALD Al₂O₃), guard ring architectures (attached, floating, and combined configurations), gettering and defect engineering methods, doping profile optimization, bias voltage management, and advanced device architectures such as pinned photodiodes and black silicon structures. A classification table organizes all the reviewed literature by material system, reduction technique, and key findings. Special emphasis is placed on silicon, germanium, III–V compounds, and emerging material photodiodes relevant to near-infrared detection, CMOS imaging, single-photon avalanche diodes (SPADs), and Time-of-Flight (ToF) applications. Full article