Search Results (2,508)

Externally bonded fiber-reinforced polymer (EB-FRP) systems have been extensively investigated for tension strengthening concrete structures. Interpretation of the available evidence remains challenging because experimental methods, specimen scales, material systems, anchorage configurations, and reporting practices vary substantially across the literature. This systematic review synthesized 174 peer-reviewed studies published between 1994 and 2026, comprising 3908 experimental test results and 42 analytical formulations addressing unanchored and anchored EB-FRP systems. Review findings showed that bond performance in unanchored systems is governed primarily by FRP stiffness, bond geometry, concrete properties, adhesive behavior, surface preparation, and environmental exposure. These parameters influence bond capacity, debonding strain, effective bond length, and failure mode. Anchored configurations consistently enhanced force transfer, delayed premature debonding, and improved load-carrying capacity relative to unanchored systems. Unanchored systems dominated the available evidence base with 3162 test results, whereas only 96 multi-anchor system tests were identified, highlighting limited understanding of anchor interaction and load redistribution mechanisms. CFRP represented the dominant material system, while substantially fewer studies investigated GFRP, BFRP, and AFRP systems. Existing strength models generally captured specific failure mechanisms within their calibration ranges but demonstrated limited transferability across different geometries, loading conditions, anchorage configurations, and environmental conditions. Limited evidence remains available for scale transfer, durability degradation, anchor strip interaction, and multi-anchor load sharing under field-representative conditions. Future research should focus on standardized benchmarking procedures, large-scale validation programs, durability-informed design approaches, experimentally validated numerical modeling, and unified design provisions for EB-FRP strengthening systems. Full article

Roadside LiDAR is a key sensing technology for intelligent transportation systems (ITSs) due to its high-precision spatial information and reliable monitoring of traffic environments. However, extracting traffic information from LiDAR point cloud data remains challenging because measurements are produced through angular sampling, causing the spacing between adjacent points to depend on radius and beam distribution. This study proposes a geometry-aware framework that incorporates LiDAR sampling geometry into the neighborhood criterion used to determine point-to-point association. The formulation defines neighborhood tolerance as a function of radial distance and vertical angular separation, enabling clustering decisions that are consistent with the sensing mechanism. In addition, the approach integrates deployment constraints based on sensor mounting height and region-of-interest limits to maintain physically meaningful connectivity under roadside sensing conditions. A systematic calibration procedure is conducted to estimate the scaling factor and radial spacing parameters and evaluate the method using both controlled and real-world datasets. Experimental results reveal that the proposed approach improves clustering accuracy and stability by reducing false negatives in sparse regions while avoiding excessive cluster merging in dense areas. The method demonstrates robust performance across varying sensing conditions and achieves higher accuracy than baseline approaches without parameter retuning, while introducing negligible computational overhead. Full article

High-speed imaging offers a non-intrusive approach for monitoring falling-film flows over horizontal tube bundles, but reflective images are difficult to quantify because grayscale variations are jointly affected by film geometry, interfacial curvature, surface slope, viewing angle, and local highlights. This study proposes an interpretable visual-proxy sensing framework for comparative state assessment of such flows. Isothermal water experiments were conducted on a five-row horizontal tube bundle over Re_Γ = 184 − 960. For each condition, grayscale frames were acquired at fps and analyzed within five fixed row-wise regions of interest. The image sequence was transformed by temporal-median background subtraction, local spatiotemporal mapping, moving-average detrending, and median-absolute-deviation normalization. The resulting normalized map M_n and dynamic renewal field G were used to extract four scalar descriptors: noise-corrected apparent renewal intensity I_R, high-frequency fraction R_HF, spectral peak frequency f_p, and burst-event rate F_B. Results show that M_n and G capture the transition from sparse column flow to more continuous sheet flow and reveal row-dependent activity organization. The descriptors provide complementary information on renewal intensity, frequency composition, dominant time scale, and intermittent events. Zero-response, noise-correction, and sensitivity tests confirm that the framework avoids structured pseudo-waves and maintains stable row-wise comparisons. The method provides a low-calibration visual sensing tool for relative falling-film state assessment. Full article

Background: Costal cartilage is the preferred structural material for augmentation rhinoplasty when robust and durable tip support is required. However, conventional full-thickness harvest is associated with significant donor-site morbidity, and commonly employed rigid fixation strategies—such as the septal extension graft—substantially restrict postoperative nasal tip compliance. The present study introduces a novel two-component technique combining a half-harvest costal cartilage procurement method with a pyramidal-shaped columellar strut graft anchored on the floating-tip principle, with the objective of maintaining postoperative nasal tip flexibility while providing structural support following augmentation rhinoplasty. Methods: A retrospective review was performed of consecutive patients who underwent primary or revision augmentation rhinoplasty using the pyramidal costal cartilage columellar strut graft technique by a single surgeon between June 2018 and February 2026. The medial half of the conjoined costal cartilage at the seventh, eighth, or ninth rib was procured via a half-harvest approach, preserving the lateral cortex and perichondrium to minimize donor-site morbidity and potential cartilage regeneration was considered a theoretical benefit. The harvested cartilage was carved into a pyramidal columellar strut and secured to the anterior nasal spine using a floating fixation construct; the inferior base of the strut was rigidly fixed to the nasal septum and anterior nasal spine with a minimum of three PDS 5-0 sutures, while the superior portion remained free to preserve physiologic nasal tip mobility. Adjunctive cap and shield grafts, perichondrial wrapping, and dermal fat grafts were employed as indicated. Primary outcomes included nasal tip projection, postoperative tip mobility, donor-site morbidity, and surgical complication rates. Results: Favorable clinical observations of maintained tip projection were noted throughout follow-up. Manual postoperative examination suggested preservation of tip flexibility in most patients; however, no validated objective mobility assessment tool was available. The revision rate for clinically significant tip deviation was low. No major donor-site adverse events—including pneumothorax or rib fracture—were encountered. Postoperative chest wall pain was minimal and transient, with most patients resuming daily activities within one week of surgery. Conclusions: The pyramidal-shaped costal cartilage columellar strut graft with half-harvest technique is a novel, biomechanically informed, and technically reproducible approach to augmentation rhinoplasty that was developed to address donor-site morbidity and postoperative tip rigidity, two commonly recognized limitations of conventional costal cartilage rhinoplasty: donor-site morbidity and postoperative nasal tip rigidity. Preservation of the lateral cortex and perichondrium during procurement may contribute to reduced postoperative donor-site discomfort, accelerates functional recovery, and may promote endogenous cartilage regeneration over time. The anatomically derived pyramidal strut geometry, combined with floating fixation to the anterior nasal spine, was designed to approximate the native columellar architecture, enabling consistent preservation of physiologic nasal tip mobility. The present series demonstrated a favorable safety profile with a low overall complication rate and an absence of major donor-site adverse events. Prospective studies with validated objective outcome measures are required to confirm these findings, to delineate the optimal patient selection criteria, and to establish evidence-based long-term outcome benchmarks for this technique. Full article

Forging is a key manufacturing route for high-performance structural components, but its process design, quality prediction, and adaptive control still rely heavily on empirical rules, offline simulations, and fragmented production data. This review examines intelligent forging from the perspective of mechanism–data–knowledge fusion, with emphasis on forging-specific process chains, real alloy systems, model validation, and industrial maturity. To improve methodological traceability, a structured literature search was conducted using Web of Science Core Collection, Scopus, ScienceDirect, SpringerLink, and Google Scholar, covering studies published from 1996 to 2026. The screened literature was organized around process perception, mechanism-based modeling, data-driven learning, hybrid modeling, knowledge representation, digital twins, online prediction, and adaptive regulation. Representative cases are discussed for closed-die forging, open-die/large forging, multistage forging, radial forging, and forging of aluminum alloys, titanium alloys, steels, and Ni-based superalloys. Particular attention is given to how specific models are validated, including independent experiments, finite-element benchmarks, industrial datasets, new geometries, sensor noise, and cross-material or cross-equipment transfer. The review further distinguishes consolidated technologies, such as FEM-based process simulation and die/preform optimization, from methods still under validation, including hybrid digital twins, sensor-updated models, and adaptive control. Large-model-assisted forging is considered a prospective direction mainly for information retrieval, case recovery, diagnostic support, and engineer-supervised recommendation rather than unsupervised real-time control. This review provides a more process-specific and critically assessed reference for developing explainable, validated, and deployable intelligent forging systems. Full article

Unsupervised learning often relies on non-negative matrix factorization (NMF) for extracting low-dimensional features. Standard deep NMF models, however, tend to miss complex hierarchical patterns and may warp the intrinsic geometry of high-dimensional data, resulting in solutions that are neither smooth nor stable. To counter these issues, we introduce DGLpSNMF—a deep graph-regularized $L_p$ smooth semi-NMF—that explicitly incorporates the data’s geometric structure via graph Laplacian regularization and $L_p$ smoothing. The optimization problem is tackled with a forward-backward splitting scheme, and we establish convergence of the generated sequence to a critical point. Experiments on four image benchmarks (JAFFE, Yale, ORL, PIE) demonstrate that DGLpSNMF consistently surpasses several state-of-the-art NMF variants in both accuracy and normalized mutual information. Full article

Automated mapping of vertical traffic signs and horizontal road markings is essential for road safety and Intelligent Transportation Systems (ITS). Traditional methods are labor-intensive, while existing automated solutions often lack a unified approach or are proprietary, limiting research accessibility and reproducibility. This paper presents a comprehensive framework for identifying these assets using a low-cost, vehicle-mounted action camera. A distance-aware frame extraction strategy is introduced to minimize data redundancy and ensure high spatial diversity. Specific strategies address the class imbalance inherent in real-world driving, ensuring robust detection for infrequent sign categories. Deep learning models handle the distinct geometries of vertical and horizontal assets, employing segmentation-based annotation for irregular road markings. Experimental results show high performance, with leading YOLO-based architectures achieving an F1-score of 0.92 for vertical signage and 0.96 for horizontal markings. By transforming raw visual data into structured georeferenced information, this framework facilitates the generation of High-Definition (HD) maps and digital inventories, supporting road authorities in proactive maintenance planning and regional road safety assessments. Full article

This paper designs a statistical channel state information-based pinching antenna system for short-packet communication (SPC). To maximize the average maximal achievable rate (MAR) under physical collision-avoidance constraints, we formulate a highly non-convex geometry optimization problem, which is solved by our proposed novel phase-domain proximal policy optimization (PPO) framework. Unlike conventional coordinate-based approaches, the agent operates in a dual-component trigonometric phase domain, and the generated phase actions are mapped to feasible antenna positions via a customized phase-domain action mapping, which fundamentally avoids the $0/2\pi$ phase discontinuity and ensures stable learning. To evaluate the reliability of SPC, we derive a tractable statistical characterization of the received signal-to-noise ratio based on a mixture Gamma approximation over spatially correlated Rician fading channels, leading to a closed-form approximation for the average block error rate (BLER). A bisection search algorithm is further developed to minimize the required blocklength under the target reliability constraint. Simulation results demonstrate that the proposed phase-domain PPO scheme significantly outperforms the conventional algorithms in terms of average MAR, average BLER, and blocklength efficiency, with the performance gain becoming more pronounced as the number of antennas per waveguide increases. Full article

Stereo matching has become an increasingly important component of modern autonomous systems. Developing deep learning-based stereo-matching models that deliver high accuracy while operating in real time continues to be a major challenge in computer vision. In the domain of cost volume-based stereo matching, accurate disparity estimation depends heavily on large-scale cost volumes. However, such large volumes store substantial redundant information and also require computationally intensive aggregation units for processing and regression, making real-time performance unattainable. Conversely, small-scale cost volumes followed by lightweight aggregation units provide a promising route for real-time performance, but lack sufficient information to ensure highly accurate disparity estimation. To address this challenge, we propose the Enhanced Shuffle Mixer (ESM) to mitigate information loss associated with small-scale cost volumes. ESM restores critical details by integrating primary features into the disparity upsampling unit. It quickly extracts features from the initial disparity estimation and fuses them with image features. These features are mixed by shuffling and layer splitting, then refined through a compact feature-guided hourglass network to recover more detailed scene geometry. The ESM focuses on local contextual connectivity with a large receptive field and low computational cost, leading to improved disparity estimation accuracy while maintaining real-time performance under the evaluated settings. The compact version of ESMStereo achieves an inference speed of 116 FPS on RTX 4070S and 91 FPS on the AGX Orin. Full article

This article addresses the role of geometry in architecture throughout history as a language that supports and connects the domains of design and aesthetic expression. This study focuses on the analysis of contemporary curvilinear glass architecture, in which geometry becomes a fundamental tool for shaping both form and its visual perception. We define and investigate panelization strategies for freeform surfaces, adopting surface continuity as the primary criterion for their classification. Research is conducted through the confrontation of two complementary approaches: a descriptive one, based on case study analysis, and a generative one, employing parametric modeling of curvilinear surfaces. In the descriptive approach, selected architectural realizations are analyzed, in which panelization strategies and their impact on the aesthetic expression of glass façades are identified. In the generative approach, a digital surface analysis is conducted, enabling the assessment of relationships between geometry, panel typology, and visual continuity. The results provide a basis for developing theoretical and methodological frameworks for the analysis and design of curvilinear glass architecture. This study identifies interdependencies between geometry, material, and fabrication processes. The main contribution of this study is a method for analyzing curved surfaces based on digital analysis, enabling a systematic evaluation of the relationships between geometry, panelization typology, and surface continuity in the context of freeform architectural design. This method may support informed and conscious design decision-making. Full article

Typhoon-induced landslides in coastal mountainous regions are controlled by the coupled effects of rainfall, wind, topography, and storm-track geometry. However, conventional static susceptibility models have limited ability to represent event-scale forcing under extreme weather conditions. This study develops a physics-guided dynamic landslide susceptibility framework and retrospectively applies it to the 2017 Tropical Cyclone Cempaka event in Pacitan Regency, Indonesia, where 743 landslides were identified. The framework integrates static terrain factors, antecedent wetness, event-scale rainfall accumulation and intensity, maximum wind speed, and a typhoon geometric exposure index derived from IBTrACS best-track information that represents track proximity, topographic shielding, rainfall-favored quadrant effects, and storm-motion effects. Under spatial block cross-validation, model performance improved progressively from the static baseline to the full-factor model, with the receiver operating characteristic area under the curve (ROC-AUC) increasing from 0.648 to 0.751, the precision–recall area under the curve (PR-AUC) reaching 0.826, and the F1-score reaching 0.744. The full-factor model also reduced missed landslide cases from 328 to 205 and concentrated predicted high-susceptibility zones along the typhoon exposure corridor. Additional parameter-sensitivity analyses further indicate that the event-based E_geo setting produced positive performance increments under the event-consistent quadrant convention. These results indicate that physically meaningful typhoon-exposure information can improve the spatial discrimination and interpretability of event-scale landslide susceptibility assessment. Full article

Two-dimensional (2D) materials have emerged as a key platform for next-generation electronics due to their atomic thickness and tunable properties. Iron disulfide (FeS₂), known as pyrite, with a bandgap of ~0.95 eV, is suitable for solar energy applications. However, its performance is limited by defects in bulk crystals. Reducing FeS₂ to a single layer eliminates bulk defects and enables strain engineering of the bandgap. In this study, First-principles density functional theory (DFT) calculations are performed using the CASTEP code and the PBEsol functional to examine the structural, electronic, and optical properties of a distorted 1T′-phase FeS₂ monolayer. Full geometry optimization yields lattice parameters a′ = 17.594 Å, b′ = 3.20231 Å, c′ = 5.28091 Å, and Fe–S bond angles of ~75.8° and ~98.2°, confirming symmetry-breaking distortion. The monolayer is dynamically stable, showing no imaginary modes in the phonon dispersion, and remains structurally intact up to 1000 K in molecular dynamics simulations. The unstrained system has an indirect bandgap of 0.70 eV, with the valence band maximum at the Γ point (dominated by S-p states) and conduction band minimum near the X point (Fe-d states). Under mechanical strain (±4%), the bandgap decreases significantly: from 0.70 eV to 0.44 eV under +4% tensile strain along the y-axis, and to 0.53 eV under −4% compressive strain. Biaxial strain causes weaker modulation, reducing the gap to 0.66 eV (+4%) and 0.62 eV (−4%). Optical absorption exceeds 10⁴ cm⁻¹ for photon energies above the bandgap, with tensile strain causing redshifts and compressive strain inducing blueshifts. These findings demonstrate that 2D FeS₂ is mechanically robust, electronically tunable, and optically active, making it a promising candidate material for flexible strain sensors and photovoltaic devices. This work is intended to motivate and inform future synthesis efforts. Full article

Secure vehicle-to-vehicle communication is essential for electric-vehicle platoons because broadcast wireless links may expose safety and control messages to passive eavesdropping. This paper investigates a physical-layer security (PLS) framework for electric-vehicle (EV) platoons under Rician fading, representing the line-of-sight conditions common in highway platooning. The proposed Jamming-Aided Cooperative Relay Selection (JACRS) framework uses an amplify-and-forward relay, destination-assisted full-duplex friendly jamming, residual self-interference modelling, and a strict total transmit power budget. Relay selection is evaluated using a full-channel state information (CSI) secrecy-selection benchmark, a practical legitimate-link CSI rule, and a deterministic platoon-geometry-aware rule based on Cooperative Adaptive Cruise Control (CACC) position information without instantaneous eavesdropper CSI. Monte Carlo simulations, supported by semi-analytical secrecy-outage and deterministic-slot benchmarks, compare the proposed scheme with Rayleigh and no-jamming amplify-and-forward (AF) baselines. Under the simulated geometry, the scheme achieves a peak ergodic secrecy rate close to 5.0 bps/Hz at 40 dBm and reduces interception risk by 78.07% relative to the Rayleigh baseline. Relay diversity reduces secrecy outage from 14.14% to 0.04% under full CSI and to 0.22% using legitimate-link CSI. The geometry-aware rule reduces the gap between practical legitimate-link selection and the full-CSI benchmark, indicating that platoon position information can improve relay selection under the tested conditions. Full article

Strict minimum message length (SMML) is an information-theoretic coding principle that represents a continuous statistical model by a finite set of assertions and a partition of the sample space. We show that the SMML objective decomposes into assertion entropy and conditional cross-entropy, balancing the cost of identifying an assertion against the cost of encoding data under the assigned model. For any fixed partition, the optimal codepoint for each cell is the model distribution that minimises Kullback–Leibler (KL) divergence from the data distribution restricted to that cell. Using the local Fisher–Rao geometry of regular parametric models, we show that, under a high-resolution LAN-scale regime, SMML partitions are asymptotically the pullback, through the maximum-likelihood estimator, of weighted Fisher–Rao Voronoi tessellations in parameter space, with assertion probabilities appearing as additive weights. For regular canonical exponential families, SMML codepoints satisfy a moment-matching condition and admit an interpretation as KL/Bregman centroids, while exact SMML cells are pullbacks of convex polyhedra in sufficient-statistic space. Together, these results show that SMML induces a natural information-geometric quantisation linking entropy-based coding, KL projection, and divergence-based Voronoi geometry. Full article

Smart warehouses require autonomous mobile robots to complete lifelong tasks while avoiding conflicts, respecting battery constraints, and sharing charging stations. Existing MAPF methods provide strong conflict resolution, but energy, charging, and online action repair are commonly evaluated separately. We present ECR-HR, an energy-constrained hybrid repair framework that combines a normalized energy model, charging-aware goals, risk-informed priorities, and bounded local conflict repair. The scientific contribution is a coupled execution and evaluation interface rather than a new complete MAPF solver or a claim of dominance over MAPF-LNS2. In reproducible simulation, we compare ECR-HR with classical, repair-based, lazy-search, conflict-based, and learning-based baselines. In 40-seed nominal evaluation, ECR-HR reduces candidate conflict rate relative to WHCA* from 0.0479 to 0.0255 ($p=3.89\times {10}^{-6}$) while MAPF-LNS2 achieves the strongest raw success. A 30-seed study using MovingAI map geometry, priority and repair comparisons, module-level runtime profiling, simulated disturbance tests, 25-seed energy coefficient sensitivity, and preference weight sensitivity further define the framework’s operating boundary. Enhanced GNN-PPO-HR increases held-out success from the original 0.188 to $0.753\pm 0.174$ but remains below mature search baselines. All evidence is simulation-based, the energy coefficients are normalized rather than hardware-calibrated, and real-robot validation remains necessary. Full article