Search Results (669)

For robotic ultrasound targeting developmental dysplasia of the hip (DDH), reconstructed infant body-surface meshes should preserve the target surface while excluding bed, support, and background geometry. This study proposes a structured vision–language model (VLM)-based multi-view segmentation method for infant phantom mesh cleanup. Text-guided 2D masks provide foreground and background evidence; mask-quality statistics and structured VLM assessments generate view-specific foreground-support weights that undergo deterministic risk suppression before target/non-target classification of 3D vertices. Hard voting and Rule-weighted voting were baselines under identical projection and post-processing settings. We evaluated one physical infant phantom across 150 separate acquisition–reconstruction trials under controlled conditions, each comprising 40 voting views and 10 held-out evaluation views, yielding 1500 manually annotated reference masks. Structured VLM voting achieved a Balanced score (intersection over union minus the 2-pixel tolerant leakage rate) of 0.804 ± 0.085, versus 0.801 ± 0.084 and 0.793 ± 0.086 for Rule-weighted and Hard voting. This leakage rate was 0.087 ± 0.043, versus 0.089 ± 0.042 and 0.092 ± 0.043; mean Coverage remained similar (0.992–0.993). The Balanced-score gain and leakage-rate reduction relative to Rule-weighted voting remained significant after Holm correction (adjusted p = 0.029 and p = 0.033, respectively). Validation using real-infant data, 3D reference standards, and closed-loop robotic scanning is still required. Full article

Recent advances in Large Language Models (LLMs) and agent-based architectures have introduced new possibilities for automating web application security testing. Unlike traditional vulnerability scanners that rely mainly on signature-based detection, LLM-powered agents can analyze application context and dynamically adapt testing strategies. This exploratory case study examined two specific laboratory-defined workflows for identifying web application security weaknesses: a human-supervised agent-based workflow and a conventional vulnerability scanner workflow. The evaluation was performed in a controlled laboratory environment against the same target application and predefined ground truth, while each workflow retained its respective application-access, discovery, and authentication mechanisms. The agent-based framework was developed using Claude Code, the Model Context Protocol, and Burp Suite Professional, while the scanner-based approach utilized Qualys Web Application Scanning. Results were mapped to a predefined ground truth covering SQL Injection, Cross-Site Scripting, and Broken Access Control and reported using Recall, Precision, and false-positive counts where estimable. The findings describe what each completed workflow showed under the tested conditions, including coverage, tester effort, and validation workload, and outline directions for further evaluation. Full article

In this paper, the authors propose a complete multiclass traffic classification model with an unknown-traffic screening method based on anomaly detection using Isolation Forest and a topological awareness method for context-based risk analysis, a dynamic trust scoring evolution, and a policy-based security action method. Topology is not fed into the traffic classifier; it is used as a contextual risk layer. The contribution is not a new classifier: XGBoost is a very popular gradient-boosted tree classifier, and the good results achieved on CICIoT2023 is a benchmark result, not a contribution. The accuracy of XGBoost is 98.99%, the Macro-F1 is 71.51%, the macro-average attack detection rate (ADR) is 70.98%, and the false positive rate (FPR) is 0.31% on CICIoT2023. Classes with high occurrence rates (e.g., DDoS, DoS, Mirai, and Vulnerability Scan) were easy to detect (F1 ≥ 90%), whereas rare classes (e.g., Uploading Attack, XSS, Backdoor Malware, SQL Injection, Browser Hijacking, Command Injection, and Recon-PingSweep, which were all below 10%) were hard to detect. The hybrid unknown-traffic module was evaluated using the leave-one-family-out method: that is, the attack family was selected and removed from the training dataset of the Isolation Forest, and then served as an unknown set for the test. The detection performance is highly family-dependent: 67 to 95 percent UTDR (new categories of behaviorally different attacks) versus only 0.1 to 3.4 percent UTDR (new variants of volumetric flood attack) with a macro-average of 51.12 percent (±43.69) and a micro-average of 2.18 percent. This dataset has a very imbalanced base rate among classes, leading to low precision for all classes (0.48 − 16.57%). The module is therefore useful to detect behaviorally novel traffic, but not necessarily novel flood-type variants. The unknown-traffic module is a high-sensitivity screen with low precision (0.48 − 16.57%); it is not a reliable unknown-threat detector. Topology analysis identified 120 nodes and 342 links (14 high risk and eight critical), and an average risk score of 0.42 was calculated. Over 60,000 samples were trusted and analyzed with respect to policy; 94.25% of observations remained in the High Trust band and 97.16% of these were attack-labeled. The trust model is thus reported as a negative result: the trust bands were unable to support a reliable benign/malicious separation. Only Alert (90.92%) and Quarantine (9.08%) were observed as policy engine actions, and there were no observations of Allow, Restrict, or Block actions; action precision, containment accuracy, and correctness of enforcement were not measured. Finally, the study results demonstrate the feasibility of the multi-stage IoT security (MSES), and reveal the obvious drawbacks to rare class recognition, unknown-threat precision, trust state coverage, and policy actions. The specific methodological contributions are: (M1) post-classification TARP: topology is separated from the classifier; (M2) confidence-gated OR-fusion: known-traffic screening for unknown traffic; and (M3) source-entity reinterpretation of user trust: traffic-only datasets. Full article

Reliable LiDAR-inertial odometry is central to UAV navigation in GNSS-denied indoor logistics, yet most benchmarks target ground vehicles and ignore the compute limits of aerial platforms. We benchmark six systems spanning two modalities and three estimator families—LiDAR-only (F-LOAM, KISS-ICP) and, within tightly coupled LiDAR-inertial estimation, filter-based (FAST-LIO2, Faster-LIO), graph-optimisation-based (liorf, from LIO-SAM) and geometric-observer-based (DLIO)—on flight data from a motion-capture laboratory and an operational warehouse. We identify and correct four ways a per-scan latency figure misleads; one misstates a system’s cost fourfold. When identical data are replayed on a workstation and on the vehicle’s computer, accuracy transfers to within 3 mm, while cost does not: the penalty tracks how far an implementation distributes its work, and on the passively cooled target it moves by up to a factor of two between sessions whose trajectories agree within millimetres. The workstation’s most accurate system collapses on the target, silently discarding up to two-thirds of its input while latency, coverage and memory report normal operation. Of the two systems that stay inexpensive, within budget and consistent with the reference, Faster-LIO is the cheaper; used as the vehicle’s position source, it reduced the onboard visual-inertial estimator’s typical error on all eight closed-loop flights. Full article

This proof-of-concept study presents the application of a novel, ultra-fast portable mid-infrared spectrometer for the non-destructive characterisation of heritage materials, including mineral-based materials, pigments, paints and polymers commonly found in artworks and historic objects. Selected spectral datasets were inspected using principal component analysis and hierarchical cluster analysis. The results enabled reliable monitoring of calcium sulfate hydration states, partial differentiation among blue and green pigment types, subtle separation of binder-based groupings between water-based and acrylic paints, and classification of synthetic foams into broad polymer families. Overall, this study shows that the technique can extract chemically relevant information in the 5000–2000 cm⁻¹ region, with acquisition speeds that may enable applications not readily achievable using conventional portable mid-infrared systems, indicating potential for in situ monitoring and classification of heritage materials in museums, historic sites and conservation studios. Future developments will expand infrared coverage, integrate scanning and combine complementary techniques. Full article

Foreign-object detection in electric vehicle wireless charging must combine visual anomaly detection with metallic-object identification while limiting the cost of scanning a coil array. This paper proposes a vision–detection-coil fusion framework that selects sensing channels from visual anomalies and evaluates metallic evidence from compensated electrical residuals. A Noisy-OR rule combines the evidence, while ambiguous metallic subtypes and incomplete inspection coverage inhibit charging. For the specified nine-channel scenario, the calculated sweep-and-decision budget is 122.12 ms, compared with 819.52 ms for 64 channels, a reduction of 85.1%. In a simulation of 3600 feature cases with the representative channel observed in every case, four-class accuracy is 82.19%, nonmetallic-class recall is 43.5%, and the object-free false-positive rate is 9.5%. Restricting acquisition to visual candidates gives 48.79% presence recall, equal to vision alone. Independent full-array inspections are therefore required; the illustrative 2-s schedule reserves 40.976% of its time for these scans. Paired comparisons show that dynamic compensation reduces disturbance-induced false alarms under the prescribed additive model. These results characterize the tradeoff between selective acquisition, material classification, and inspection coverage. These numerical evaluations do not establish physical detection limits or thermal safety. Full article

Accurate radar-based quantitative precipitation estimation (QPE) plays a critical role in disaster prevention and reduction. In mountainous terrain, blockage of low-elevation radar beams can lead to missing low-level precipitation observations, while errors in specific differential phase K_DP retrieval further limit QPE accuracy. To address these issues, this study uses data from two S-band dual-polarization weather radars located in Fangchenggang and Beihai, together with ground rain gauges, local raindrop size distribution (DSD) data, and digital elevation model (DEM) data. Hybrid-scan elevation construction and dual-radar mosaicking are adopted to improve the effective coverage of low-elevation observations, and a threshold-fusion strategy for K_DP, denoted as K_{DP_TF}, is proposed. The strategy selects the larger of the conventionally quality-controlled K_{DP_QC} and variationally reconstructed K_{DP_VR} at each valid gate. Based on the CSU-HIDRO algorithm, three QPE products, namely the quality-control QPE (QC_QPE), the variational-reconstruction QPE (VR_QPE), and the threshold-fusion QPE (TF_QPE), are generated. The precipitation events associated with Typhoon Yagi in 2024 and Typhoon Matmo in 2025 are selected for evaluation. The results indicate that TF_QPE most effectively mitigates the underestimation of 24 h accumulated precipitation. Compared with QC_QPE, the magnitude of the negative relative bias (RB) of TF_QPE is reduced, from −27.30% to −20.07% and from −16.56% to −9.78%, respectively, for the Yagi and Matmo events; the root-mean-square error (RMSE) decreases from 46.76 to 43.79 mm and from 43.27 to 43.13 mm, respectively. The hourly precipitation results further show that TF_QPE better captures the peak intensity of heavy precipitation and exhibits no evident temporal lag relative to rain gauge observations. For the heavy precipitation category, TF_QPE also yields the highest probability of detection (POD) and critical success index (CSI). These results suggest that the QPE framework developed in this study, which integrates hybrid-scan elevation construction, dual-radar mosaicking, and threshold-fusion-based K_DP optimization, can improve the effective low-elevation observational coverage in mountainous terrain and mitigate the underestimation of typhoon-induced heavy precipitation in radar QPE. These results provide technical references for typhoon-related heavy-rainfall monitoring and flood early-warning as well as radar-QPE applications over coastal Guangxi. Full article

Outdoor mobile spraying robots are increasingly being used for precision plant protection and vector-borne disease control. Coverage planning in unstructured environments remains difficult because spray models are computationally costly, chassis motion is subject to multiple constraints, and pan-tilt spraying must be coordinated with vehicle motion. This paper proposes a hierarchical planning method to address these challenges. The spray model uses a 2D fan-shaped representation for chassis planning and a 3D conical representation for pan-tilt control. The chassis planner then generates local paths, optimizes the operation sequence with a genetic algorithm and a Dijkstra distance matrix, and adjusts the resulting trajectory with Hybrid A*. An N-shaped scanning strategy is coupled with chassis pan-tilt coordination for target-surface coverage. In simulation, the simplified coverage models achieve Jaccard agreement above 90% with the corresponding Monte Carlo reference regions. Across three chassis scenarios, the complete framework attains 98.1% average binary effective-area coverage, an 18.9 percentage-point difference relative to conventional Hybrid A* in this benchmark. The three modeled pan-tilt surfaces reach 100% binary threshold coverage under the stated geometric model. These results indicate feasibility at the simulation level under the stated geometric and windless assumptions. They do not measure physical tracking accuracy, deposition uniformity, or outdoor field performance. Full article

Wide-area laser cleaning requires uniform surfaces within individual cleaning tracks and across their boundaries. This study investigated the effects of traverse speed, galvanometer scan frequency, and track overlap on 0.5 mm thick TC4 sheets cleaned with a 1000 W multimode nanosecond laser. Increasing traverse speed from 4 to 8 mm/s at 9 Hz reduced surface crack networks, while scan frequencies of 12–15 Hz improved surface uniformity at 6 mm/s. A supplementary experiment at 8 mm/s and 15 Hz yielded a representative EDS point oxygen content of 6.77 wt.%. The mean edge transition width was 493 μm, corresponding to a geometric overlap estimate of 0.986 mm. However, a distinct boundary and local oxygen enrichment persisted at a 1 mm overlap. Across three independent experiments, the mean oxygen contents of the overlap regions were 12.15 ± 1.02 wt.% at 1 mm and 7.68 ± 0.20 wt.% at 2 mm after excluding suspected acquisition anomalies. Fine cracks remained visible at 500× magnification at 2 mm, whereas no boundary was discernible in the observed SEM field at 3 mm. At 8 mm/s and 15 Hz with a 100 mm scan length, 2 mm was the smallest tested overlap that satisfied the combined morphological and relative oxygen-content criteria. This result provides an operating recommendation for the tested conditions and shows that geometric coverage alone is insufficient to ensure boundary uniformity. Full article

As distributed energy resources (DERs) and DER aggregators are integrated into distribution systems, the attack surface of critical power infrastructure expands. Built-in cryptography is effective against some false data injection (FDI) attacks, man-in-the-middle attacks, and tampered data from unauthenticated sources but cannot detect denial of service (DoS) attacks or FDI attacks originating from already-authenticated devices. This paper proposes a Dual-Tier Cyberattack Detection (DTCD) algorithm implemented within a DER aggregator that detects cyberattacks within the cyber and physical layers of a DER-integrated distribution system. The first tier, or cyber filter (CF), applies rule-based cyber checks to detect protocol-level DoS attacks, including TCP floods, SYN floods, and port scan attacks, and timestamp-based FDI anomalies. The second tier, or voltage anomaly detector (VAD), applies a physics-based Gradient Boosting Machine (GBM) dual-fusion model to detect event-driven FDI attacks by evaluating whether observed bus voltage deviations can be explained by expected load or photovoltaic (PV) changes. The algorithm is validated in real-time hardware-in-the-loop (HIL) testing on the PNNL powerNET cyberphysical testbed using an IEEE 123 bus model with DNP3 communication. CF detects all four tested attack signatures under partially mitigated traffic conditions with sub-50 ms detection times. VAD achieves a pooled F1-score of 0.875 and a receiver operating characteristic area under the curve (ROC AUC) of 0.802 across six attack scenarios at three operating conditions and four test days. VAD completes analysis in 2363 ms on average, within the sub-SCADA timeline. A two-tier comparison study shows that DTCD’s cyber check and physics-based tiers outperform cryptographic and signature-based intrusion detection approaches in the first tier and a standalone-GBM approach in the second tier. The two tiers operate in parallel and provide complementary coverage of DoS and FDI attacks. Replacing CF misses timestamp anomalies, and replacing VAD lowers the FDI F1-score. Results demonstrate that the proposed algorithm effectively complements cryptographic prevention as a detection layer, can provide cybersecurity coverage for legacy devices through aggregator-level packet screening, and completes its analysis within the sub-SCADA operational window. Full article

To address the problems of preferential acid channeling, limited treatment coverage, and nonuniform stimulation during acidizing of fractured carbonate reservoirs, a viscoelastic surfactant (VES) self-diverting acid system was investigated with emphasis on the relationship between its microstructure and rheological properties, as well as its effectiveness in core acidizing. The rheological properties and microstructure of the VES self-diverting acid were characterized by rheological measurements and cryogenic scanning electron microscopy. Core-flooding experiments, nuclear magnetic resonance (NMR) T₂ spectroscopy, and computed tomography (CT) scanning were conducted to investigate the evolution of pore structure before and after acidizing. The results show that the acid breakthrough volume exhibits a V-shaped trend with increasing injection rate, initially decreasing and then increasing. The optimal injection-rate range under the investigated reservoir conditions was determined to be 1.5–2.0 mL/min. After acid flooding at the optimal injection rates, the higher-T₂ peak area increased by 11.97–23.64%, while the lower-T₂ peak area increased by 25.72–28.97%. Quantitative CT analysis showed that the fracture volume of all tested cores increased by more than 120%, and dissolution-enlarged zones of different sizes as well as small branching channels were observed in the CT slices. These results demonstrate that VES self-diverting acid can effectively enhance acid coverage and improve stimulation uniformity in fractured carbonate rocks, providing experimental evidence and theoretical support for acidizing-based reservoir stimulation. Full article

A 60-year-old male patient presented seeking replacement of missing posterior teeth and improvement of his smile. Clinical findings included partial edentulism, bilateral posterior crossbite, secondary caries, defective restorations, anterior and posterior tooth wear, posterior gingival inflammation, supraeruption of the mandibular posterior teeth, and unfavorable inclination of the mandibular posterior teeth. An interdisciplinary treatment plan combining orthodontic, implant, and restorative therapy was selected to improve tooth position, occlusion, restorative space, and esthetics before definitive rehabilitation. Following orthodontic treatment, a digital workflow incorporating intraoral scanning, digital wax-up, restorative space evaluation, and computer-aided design was used to plan a full-mouth rehabilitation. Implant-supported restorations replaced the missing posterior teeth, while the remaining dentition was restored with tooth-supported restorations. Partial-coverage restorations and occlusal veneers were used posteriorly to preserve sound tooth structure, and minimally invasive labial veneers were used anteriorly to improve esthetics. Zirconia was selected because of its favorable mechanical properties and clinical versatility. Cementation was performed under complete dental dam isolation to minimize contamination. This case illustrates how a staged interdisciplinary approach supported by digital planning can facilitate conservative full-mouth rehabilitation and maintain satisfactory functional and esthetic outcomes over a 3-year follow-up. Full article

Patients receiving palliative radiotherapy (PRT) often experience substantial symptom burden and may face challenges attending multiple hospital visits. Planning treatment using an existing diagnostic computed tomography (dCT) scan instead of a dedicated computed tomography (CT) simulation may improve access to timely care. This prospective feasibility study evaluated a dCT-based planning workflow using kilovoltage (kV) image verification for patients with bone metastases, with potential applicability to resource-limited settings. Ten patients receiving conventional PRT (8 Gy in 1 fraction or 20 Gy in 5 fractions) with an eligible dCT acquired within 28 days were enrolled. As part of this feasibility study, all patients also underwent standard CT simulation, which served as the dosimetric comparator and fallback treatment pathway. Radiotherapy plans generated on dCT and CT simulation scans were compared for dosimetric quality, while treatment deliverability, patient satisfaction, and estimated carbon emissions were assessed. Seven of ten patients were successfully treated using the dCT workflow. The three remaining patients were transitioned to standard CT simulation treatments due to an uncorrectable dCT image rotation in the planning software (n = 1), scheduling constraints (n = 1), and a larger-than-anticipated difference in patient separation on the treatment unit (n = 1). Target coverage and organ-at-risk doses were comparable between dCT and CT simulation plans, satisfaction among dCT-treated patients was high, and potential avoidance of CT simulation was estimated to reduce carbon emissions by approximately 10 kg carbon dioxide equivalent (CO₂e) per patient. These findings demonstrate that dCT-based planning with kV image verification is feasible, provides dosimetry comparable to conventional CT simulation, and warrants further evaluation as a strategy to improve access to timely PRT while reducing healthcare resource use and environmental impact. Full article

Frozen soil creep is a primary factor affecting the long-term stability of cold-region engineering. Based on a structured, PRISMA-informed search of Web of Science, Scopus, CNKI, and ScienceDirect (last searched 31 August 2026; coverage 1950–2026), 126 peer-reviewed papers were included after screening 243 de-duplicated records (296 records initially identified; 53 duplicates removed). This paper reviews recent advances in frozen soil creep from four perspectives: experimental methods, micro-mechanisms, mechanical characteristics, and constitutive models. In experiments, laboratory temperature control has expanded from uniform fields to temperature gradient fields, and field monitoring achieves creep separation and multi-parameter sensing via plate load tests, layered settlement, and distributed optical fibers. At the micro-scale, techniques such as Computed Tomography (CT), Scanning Electron Microscopy (SEM), Nuclear Magnetic Resonance (NMR), and Electrical Resistivity Tomography (ERT) infer the synergistic mechanisms of unfrozen water lubrication, mass-transport medium for solutes and particles, and breakdown of ice cementation across the three creep stages. Concerning mechanical characteristics, the influences of temperature, water content, confining pressure, loading conditions, and salinity on creep rate and failure are summarized; the determination methods and influencing factors of long-term strength are analyzed; and the physical bases and applicability of various strength criteria are compared. In terms of models, the theoretical frameworks, key advances, and application boundaries of empirical models, element models, and classical theoretical models are reviewed. Finally, it is indicated that future breakthroughs lie in constructing a cross-scale bridge of “micro-mechanisms–meso-representation–macro-response” and developing a unified constitutive model that adopts key mechanism parameters as internal variables while balancing physical interpretability and parameter accessibility. Full article

UAV-based coverage missions near exclusion zones—such as no-spray buffer zones in precision agriculture (watercourse margins, residential setbacks, organic field boundaries, apiary protection areas, and power line corridors), urban no-fly zones, and post-disaster cordons—demand coverage path planning (CPP) that maximizes spray coverage while strictly avoiding regulatory violations. These buffer zones are typically delineated as rectangular exclusion regions, creating abrupt obstacle edges where conventional uniform grid decomposition faces a fundamental dilemma: coarse grids leave untreated margins that become pest and disease reservoirs, while fine grids incur prohibitive computational cost. Furthermore, deadlock situations frequently arise in fields fragmented by multiple buffer zones, and naive A*-based escape maneuvers waste flight time without contributing spray coverage. This paper proposes an adaptive CPP framework integrating environment modeling, edge refinement, and deadlock escape into a unified three-level optimization. First, a spray-swath-driven adaptive grid decomposition dynamically couples grid resolution with the spray swath width, enabling efficient coverage of large open areas while preserving fine resolution near buffer zone boundaries. Second, a multi-level threshold hierarchical edge refinement strategy classifies boundary-adjacent void regions by size, applying targeted path interpolation, local re-planning, and region-level CPP to achieve boundary-tight coverage without encroaching into exclusion zones. Third, an improved A* deadlock escape algorithm selects the escape target by a goal-evaluation cost combining a Euclidean distance term and a binary obstacle-adjacency indicator, guiding the UAV along escape trajectories that advance the spray mission. Simulation experiments on four representative farmland scenarios demonstrate that the proposed method achieves 100% coverage in all test cases while reducing path length by approximately 48% and scan repetition by over 70% compared with fixed-grid baselines. These results are obtained under idealized simulation conditions and have not yet been validated in field trials; operational benefits such as pesticide savings are, therefore, estimated under the specified assumptions. Full article