Search Results (851)

The performances of two autosegmentation algorithms were evaluated on 28 anonymized pelvic CT scans as a pilot study for the clinical implementation of a semi-automatic workflow. Four organs at risk (OARs), namely the rectum, bladder, and femoral heads, were contoured manually by an expert radiation oncologist (RO)—considered as the ground truth (GT)—and by model-based segmentation (MBS) and deep learning (DL) algorithms. Autocontouring performances were evaluated using a qualitative scoring system, contouring time analysis, and five geometrical indices: the 95^th percentile Hausdorff Distance (95HD), Dice Similarity Coefficient (DSC), Surface Dice Similarity Coefficient (SDSC), Added Path Length (APL), and Relative Added Path Length (RAPL). Considering total median value for the four OARs, both MBS and DL showed clinically acceptable results with differences between the two algorithms being not statistically significant for almost all indices. The DL autocontouring algorithm achieved high geometric accuracy, high scores from the ROs, and consistent performances with all validation indices for every OAR. The MBS algorithm achieved high geometric accuracy for the femoral heads and bladder. The DL algorithm required 30 s to contour all the OARs, and the MBS algorithm required 90 s, showing a time gain compared with the manual contours, which took 20 min for each case. The DL autocontouring algorithm obtained promising but preliminary results with every evaluation metric and for every analyzed OAR. The application of the MBS algorithm as the only contouring tool still presents challenges. Full article

The utilization of polymer-based additive manufacturing processes for the production of functional components, consumer goods, spare parts, etc., has increased thanks to recent technological advances. The Arburg Plastic Freeforming (APF) process is a promising AM technology, in which standard plastic granules are deployed, and droplets are discharged along a track instead of using continuously extruded straws, unlike other filament-based processes, to the benefit of various industries that require good mechanical properties while maintaining dimensional precision. Due to the round shape of the droplets and tracks, however, defects such as voids can occur between individual paths during processing, which affect, most notably, mechanical properties. The electrical/ferroelectric properties of conductive/electroactive polymers are also affected. This study focuses on determining the optimal form factor for processing a special grade polyvinylidene fluoride (PVDF) material whilst other parameters, along with the ones ascertained in previous work, are kept constant. Along with tensile tests, X-ray computed microtomography (µ-CT) and scanning electron microscopy (SEM) analyses are implemented, particularly to observe microstructural porosity. Electrical properties and possible piezoelectric behavior are investigated via an originally adapted analytical method. The results provide important insights into the APF process and printing high-performance plastics with individual features, expanding the potential for further applications. Full article

To address the severe surface imperfections induced during ultrafast pulsed laser fabrication of fused silica microfluidic chips, a high-precision CO₂ laser polishing strategy based on shallow-layer melting and reflow was employed. This method enables localized melting within an extremely thin surface layer, effectively smoothing the topography without altering the original microstructure geometry. An L9(3³) orthogonal experimental design was conducted to systematically investigate the influence of key parameters on polishing quality, identifying defocus distance as the dominant factor affecting surface roughness, followed by scanning speed and laser power. The optimal parameter combination was determined to be a laser power of 8 W, a defocus distance of 6 mm, and a scanning speed of 5 mm/s. Furthermore, an overlap rate between 38% and 63% was found to ensure sufficient fusion without excessive remelting, with the minimum surface roughness of 0.157 µm achieved at a 50% overlap rate. Based on the optimized parameters, adaptive scanning paths were designed for different functional units of a fused silica microfluidic chip. Surface characterization demonstrated that the surface roughness was remarkably reduced from 303 nm to 0.33 nm, meeting optical-grade surface quality requirements. Full article

Background: Intraventricular tumors represent a minority in the context of brain tumors, but their surgical treatment is particularly complex due to their vascularization and visualization, especially in deep localization. The characteristics of these tumors make them ideal candidates for minimally invasive surgical strategies such as the tubular retractor technique, above all in the elderly population. Objectives: A 1-year multi-center, retrospective case series was performed: the authors describe their preliminary experience using a neuronavigated tubular retractor in the management of 11 cases of intraventricular meningiomas. Methods: Clinical and radiological findings were examined to define the outcomes. We used an alternative tubular retractor system obtained using a modified preexisting general surgery trocar (ENDOPATH XCEL 15 mm trocar) or the NICO System BrainPath. Results: Gross total resection, defined as the removal of all the tumor visible from the brain scans, was achieved in all cases. Ten out of eleven of the patients did not experience major complications or permanent neurological deficits. Four patients presented transitory post-operative agitation, visual blurring and transient hemiparesis. All patients (mean age 72.6 years) were discharged from the hospital in 5–7 days. Conclusions: Our preliminary experience suggests that the use of navigated tubular retractors, by displacing the fibers and hence minimizing the damage to the surrounding cerebral parenchyma, is feasible and safe, representing a minimally invasive technique for a personalized and patient-tailored approach. The use of the selective ultrasonic aspirator makes it possible to excise the tumor through the narrow corridor of the tubular lumen of around 2 cm, and this technique can also be improved using both endoscope and microscope guidance. Full article

Compact antennas with ultra-wideband operation and stable radiation are essential for portable and airborne ground-penetrating radar (GPR), yet miniaturization in the sub 3 GHz region is strongly constrained by the wavelength-driven aperture requirement and often leads to impedance discontinuity and radiation instability. This paper presents a compact aperture-slot antipodal Vivaldi antenna (AS-AVA) designed under a radiation stability-driven co-design strategy, where the miniaturization features are organized along the energy propagation path from the feed to the flared aperture. The proposed structure combines (i) aperture-slot current-path engineering with controlled meandering to extend the low-frequency edge, (ii) four tilted rectangular slots near the aperture to restrain excessive edge currents and suppress sidelobes, and (iii) back-loaded parasitic patches for coupling-based impedance refinement to eliminate residual mismatch pockets. A fabricated prototype on FR-4 (thickness 1.93 mm) occupies $111.15\times 156.82$ mm² and achieves a measured $S_{11}$ below $-10$ dB from 0.63 to 2.03 GHz (fractional bandwidth 105.26%). The measured realized gain increases from 2.1 to 7.5 dBi across the operating band, with stable far-field radiation patterns; the group delay measured over 0.6–2.1 GHz remains within 4–8 ns, indicating good time-domain fidelity for stepped-frequency continuous-wave (SFCW) operation. Finally, the antenna pair is integrated into an SFCW-GPR testbed and validated in sandbox and outdoor experiments, where buried metallic targets and a subgrade void produce clear B-scan signatures after standard processing. These results confirm that the proposed AS-AVA provides a practical trade-off among miniaturization, broadband matching, and radiation robustness for compact sub 3 GHz GPR platforms. Full article

Multiple Unmanned Aerial Vehicle (UAV) collaborative coverage path planning is widely applied in fields such as regional surveillance. However, optimizing the trade-off between deployment costs and task execution efficiency remains challenging. To balance resource costs and execution efficiency with an uncertain number of UAVs, this paper analyzes the characteristics of irregular mission areas and formulates a bi-level optimization model for multi-UAV collaborative CPP. The model aims to minimize both the number of UAVs and the total path length. First, in the upper level, an improved Best Fit Decreasing algorithm based on binary search is designed. Straight-line scanning paths are generated by determining the minimum span direction of the irregular regions. Task allocation follows a longest-path-first, minimum-residual-range rule to rapidly determine the minimum number of UAVs required for complete coverage. Considering UAV’s turning radius constraints, Dubins curves are employed to plan transition paths between scanning regions, ensuring path feasibility. Second, the lower level transforms the problem into a Multiple Traveling Salesman Problem that considers path continuity, range constraints, and non-overlapping path allocation. This problem is solved using an Improved Biased Random Key Genetic Algorithm. The algorithm employs a variable-length master–slave chromosome encoding structure to adapt to the task allocation of each UAV. By integrating biased crossover operators with 2-opt interval mutation operators, the algorithm accelerates convergence and improves solution quality. Finally, comparative experiments on mission regions of varying scales demonstrate that, compared with single-level optimization and other intelligent algorithms, the proposed method reduces the required number of UAVs and shortens the total path length, while ensuring complete coverage of irregular regions. This method provides an efficient and practical solution for multi-UAV collaborative CPP in complex environments. Full article

Diffraction-based velocity analysis is a key data interpretation technique in geophysical exploration, typically relying on the geometric characteristics, energy distribution, or propagation paths of diffraction waves. The hyperbola-based method is a classical strategy in this category, which extracts depth-dependent velocity (or dielectric properties) by correlating the hyperbolic shape of diffraction events with subsurface parameters for characterizing subsurface structures and material compositions. In this study, we propose a layer-stripping velocity analysis method applicable to ground-penetrating radar (GPR) and lunar-penetrating radar (LPR) data, with two main innovations: (1) replacing traditional local optimization algorithms with an intuitive parallelism check scheme, eliminating the need for complex nonlinear iterations; (2) performing depth-progressive velocity scanning of radargram diffraction signals, where shallow-layer velocity analysis constrains deeper-layer calculations. This strategy avoids misinterpretations of deep geological objects’ burial depth, morphology, and physical properties caused by a single average velocity or independent deep-layer velocity assumptions. The workflow of the proposed method is first demonstrated using a synthetic rock-fragment layered model, then applied to derive the near-surface dielectric constant distribution (down to 27 m) at the Chang’e-4 landing site. The estimated values range from 2.55 to 6, with the depth-dependent profile revealing lunar regolith stratification and interlayer material property variations. Consistent with previously reported results for the Chang’e-4 region, our findings confirm the method’s applicability to LPR data, providing a new technical framework for high-resolution subsurface structure reconstruction. Full article

The remote monitoring and quantification of industrial gas emissions, such as sulfur dioxide (SO₂), are critical for environmental protection. This research demonstrates an integrated methodology for estimating SO₂ emission rates (kg/s) from an industrial chimney using passive Fourier transform infrared (FTIR) spectroscopy combined with atmospheric dispersion modeling. Infrared spectra were acquired at a stand-off distance of 570 m within the 7–14 $\mathsf{\mu }$m spectral range at a resolution of 4 cm⁻¹. Path-integrated SO₂ concentrations were determined through cross-sectional scanning of the gas plume. To translate these optical measurements into an emission rate, the atmospheric dispersion of the plume was modeled using the Pasquill–Briggs approach, incorporating source parameters and meteorological data. Over two experimental series, the calculated average SO₂ emission rates were 15 kg/s and 22 kg/s. While passive FTIR spectroscopy has long been applied to remote gas detection, this work demonstrates a consolidated framework for retrieving industrial emission rates from stand-off, line-integrated measurements under real industrial conditions. The proposed approach fills a niche between local in-stack measurements and large-scale remote sensing systems, which contributes to the development of flexible ways to monitor industrial emissions. Full article

Visual search behavior, influenced by expertise, prior knowledge, training, and visual fatigue, is crucial in ophthalmic diagnostics. This study investigates differences in eye-tracking strategies between novice and experienced eye care practitioners during fundus image interpretation. Forty-seven participants, including 37 novices (first- to fourth-year optometry students) and 10 experienced optometrists (≥2 years of experience), viewed 20 fundus images (10 normal, 10 abnormal) while their eye movements were recorded using an Eyelink1000 Plus gaze tracker (2000 Hz). Diagnostic and laterality accuracy were assessed, and statistical analyses were conducted using Sigma Plot 12.0. Results showed that experienced practitioners had significantly higher diagnostic accuracy (83 ± 6.3%) than novices (70 ± 12.9%, p < 0.005). Significant differences in oculomotor behavior were observed, including median latency (p < 0.001), while no significant differences were found in median peak velocity (p = 0.11) or laterality accuracy (p = 0.97). Diagnostic accuracy correlated with fixation count in novices (r = 0.54, p < 0.001), while laterality accuracy correlated with total dwelling time (r = −0.62, p < 0.005). The experienced practitioners demonstrated systematic and focused visual search patterns, whereas the novices exhibited unorganized scan paths. Enhancing training with visual feedback could improve fundus image analysis accuracy in novice clinicians. Full article

Background/Objectives: Gunshot wounds in living patients present significant challenges from both a clinical and a forensic perspective. Understanding the exact trajectory of a bullet is crucial not only for guiding treatment but also for providing reliable documentation in legal settings. This work introduces a practical diagnostic workflow that combines OsiriX (V. 14.1.1), a DICOM viewer with advanced 3D tools, with Autodesk Maya, a modeling platform used to recreate the external shooting scene. Methods: CT scans obtained with multidetector systems were analyzed in OsiriX using a structured, seven-step process that included multiplanar reconstructions, 3D renderings, and region-of-interest tracking. The reconstructed trajectories were then exported to Maya, where they were integrated into a virtual model of the shooting scene to correlate internal findings with the incident’s external dynamics. Results: The workflow allowed precise identification of entry and exit points, reliable reconstruction of bullet paths, and effective 3D visualization. While OsiriX provided detailed information for clinical and radiological purposes, the use of Maya enabled simulation of the external scene, improving forensic interpretation and courtroom presentation. The procedure proved reproducible across cases and compatible with emergency timelines. Conclusions: The combined use of OsiriX and Maya offers a reproducible and informative method for analyzing gunshot wounds in living patients. This approach not only supports surgical and diagnostic decisions but also enhances the forensic value of radiological data by linking internal trajectories to external shooting dynamics. Its integration into trauma imaging protocols and forensic workflows could represent a significant step toward standardized ballistic documentation. Full article

This study evaluates whether an AI-built DeFi website case can match professionally developed DeFi platforms in digital marketing performance, innovation-related strategic behavior, and entrepreneurial impact. Using a multi-method design, we compare five established DeFi websites (Aave, Lido, Curve, MakerDAO, Uniswap) against one AI-built interface (Nexus Protocol). The analysis is designed as a five-platform benchmarking study of established professional DeFi websites, complemented by one AI-built case (Nexus Protocol) used as an illustrative comparison rather than a representative class of AI-built interface. The objectives are to (i) test differences in traffic composition and acquisition strategies, (ii) quantify how engagement signals predict authority and branded traffic, (iii) examine cognitive processing and trust-cue attention via eye tracking, and (iv) model emergent engagement and authority dynamics using agent-based simulation (ABM). Web analytics (March–October 2025) show significant variation in traffic composition across professional platforms (ANOVA F = 3.41, p = 0.0205), while regression models indicate that time on site and pages per visit positively predict Authority Score (R² = 0.61) and Branded Traffic (R² = 0.55), with bounce rate exerting an adverse effect. PCA and k-means clustering identify three strategic archetypes (innovation-driven, balanced-growth, efficiency-focused). Eye-tracking results show that professional interfaces generate tighter fixation clusters and shorter scan paths, indicating higher cognitive efficiency. In contrast, fixation on key UI elements and trust cues is comparable across interface types. ABM outputs further suggest that reduced engagement depth in the AI-built interface yields weaker long-run branded-traffic and authority trajectories. Overall, the study provides an integrated evaluation framework and evidence-based implications for AI-driven interface design in high-trust fintech environments. Full article

Background: Digital intraoral scanning has become an essential component of modern restorative dentistry, offering enhanced accuracy, workflow efficiency, and patient comfort compared to conventional impression techniques. Despite these advantages, the accuracy of intraoral scanners (IOS) can be affected by multiple parameters, among which scanning strategy and device design are particularly influential. Purpose: This study aimed to investigate the effect of different scanning strategies on scan accuracy and precision, focusing on two widely used intraoral scanners (Medit i700 and Trios 5) in a controlled in vitro environment. Materials and Methods: A standardized digital test object was created according to ISO 20896-1 specifications to ensure uniformity and comparability. The object was printed using a high-precision 3D printer and scanned multiple times with both IOS systems, employing distinct scanning strategies under identical environmental conditions. Data analysis was performed using descriptive and comparative statistics, including Mean, Median, Mean Absolute Deviation (MAD), Root Mean Square Error (RMSE), Standard Deviation (SD), and Variance, to evaluate trueness and precision. Results: The Medit i700 consistently exhibited lower deviation values and greater precision compared with the Trios 5, reflecting higher trueness and precision. Scanning strategy influenced scan outcomes; structured, systematic scanning paths produced more stable and accurate datasets. The Trios 5 demonstrated higher variability, suggesting increased sensitivity to operator motion and scanning trajectory. Conclusions: Both the scanner type and scanning strategy substantially affect intraoral scan accuracy. The superior performance of the Medit i700 indicates greater robustness and operator-independent stability. Clinically, these results underscore the importance of standardized scanning protocols, as operator consistency may be a key determinant of digital impression accuracy and, consequently, of clinical outcomes. Full article

Objectives: The aim of this study was to present and describe a digital workflow integrating Digital Smile Design (DSD) with computer-aided design/computer-aided manufacturing (CAD/CAM) and additive manufacturing technologies for the fabrication of dental preparation guides, focusing on workflow feasibility, design reproducibility, and clinical handling. Materials and Methods: A digital workflow was implemented using intraoral scanning and Exocad DentalCAD 3.1 Elefsina software to design dental preparation guides based on digitally planned restorations. Preparation margins, insertion paths, and minimal material thickness were defined virtually. The guides were fabricated using both subtractive (PMMA milling) and additive (stereolithographic-based 3D printing) manufacturing techniques. Post-processing included chemical cleaning, support removal, additional light curing, and manual finishing. The evaluation was qualitative and descriptive, based on visual inspection, workflow performance, and guide adaptation to printed models. Results: The proposed digital workflow was associated with consistent fabrication of preparation guides and predictable transfer of the virtual design to the manufactured guides. Digital planning facilitated clear visualization of preparation margins and insertion axes, supporting controlled and minimally invasive tooth preparation. The workflow demonstrated good reproducibility and efficient communication between clinician and dental technician. No quantitative measurements or statistical analyses were performed. Conclusions: Within the limitations of this qualitative feasibility study, the integration of DSD with CAD/CAM and 3D printing technologies represents a viable digital approach for designing and fabricating dental preparation guides. The workflow shows potential for improving predictability and communication in restorative dentistry. Full article

To address the challenges in unstructured orchard environments, including high geometric similarity between fruit trees (with the measured average Euclidean distance difference between point cloud descriptors of adjacent trees being less than 0.5 m), significant dynamic interference (e.g., interference from pedestrians or moving equipment can occur every 5 min), and uneven terrain, this paper proposes an improved mapping algorithm named OSC-LIO (Orchard Scan Context Lidar Inertial Odometry via Smoothing and Mapping). The algorithm designs a dynamic point filtering strategy based on Euclidean clustering and spatiotemporal consistency within a 5-frame sliding window to reduce the interference of dynamic objects in point cloud registration. By integrating local semantic features such as fruit tree trunk diameter and canopy height difference, a two-tier verification mechanism combining “global and local information” is constructed to enhance the distinctiveness and robustness of loop closure detection. Motion compensation is achieved by fusing data from an Inertial Measurement Unit (IMU) and a wheel odometer to correct point cloud distortion. A three-level hierarchical indexing structure—”path partitioning, time window, KD-Tree (K-Dimension Tree)”—is built to reduce the time required for loop closure retrieval and improve the system’s real-time performance. Experimental results show that the improved OSC-LIO system reduces the Absolute Trajectory Error (ATE) by approximately 23.5% compared to the original LIO-SAM (Tightly coupled Lidar Inertial Odometry via Smoothing and Mapping) in a simulated orchard environment, while enabling stable and reliable path planning and autonomous navigation. This study provides a high-precision, lightweight technical solution for autonomous navigation in orchard scenarios. Full article

Modern Earth observation combines high spatial resolution, wide swath, and dense temporal sampling, producing image grids and sequences far beyond the regime of standard vision benchmarks. Convolutional networks remain strong baselines but struggle to aggregate kilometre-scale context and long temporal dependencies without heavy tiling and downsampling, while Transformers incur quadratic costs in token count and often rely on aggressive patching or windowing. Recently proposed visual state-space models, typified by Mamba, offer linear-time sequence processing with selective recurrence and have therefore attracted rapid interest in remote sensing. This survey analyses how far that promise is realised in practice. We first review the theoretical substrates of state-space models and the role of scanning and serialization when mapping two- and three-dimensional EO data onto one-dimensional sequences. A taxonomy of scan paths and architectural hybrids is then developed, covering centre-focused and geometry-aware trajectories, CNN– and Transformer–Mamba backbones, and multimodal designs for hyperspectral, multisource fusion, segmentation, detection, restoration, and domain-specific scientific applications. Building on this evidence, we delineate the task regimes in which Mamba is empirically warranted—very long sequences, large tiles, or complex degradations—and those in which simpler operators or conventional attention remain competitive. Finally, we discuss green computing, numerical stability, and reproducibility, and outline directions for physics-informed state-space models and remote-sensing-specific foundation architectures. Overall, the survey argues that Mamba should be used as a targeted, scan-aware component in EO pipelines rather than a drop-in replacement for existing backbones, and aims to provide concrete design principles for future remote sensing research and operational practice. Full article