Search Results (17,519)

High-fidelity 3D reconstruction of human-sized objects typically requires multi-sensor scanning systems that are expensive, complex, and rely on proprietary hardware configurations. Existing low-cost approaches often rely on handheld scanning, which is inherently unstructured and operator-dependent, leading to inconsistent coverage and variable reconstruction quality. This limitation necessitates the need for a controlled, repeatable, and affordable scanning method that can generate high-quality data without requiring multi-sensor hardware or external tracking markers. This study presents a marker-less scanning platform designed for human-scale reconstruction. The system consists of a single structured-light sensor mounted on a vertical linear actuator, synchronised with a motorised turntable that rotates the subject. This constrained kinematic setup ensures a repeatable cylindrical acquisition trajectory. To address the geometric ambiguity often found in vertical translational symmetry (i.e., where distinct elevation steps appear identical), the system employs a sensor-assisted initialisation strategy, where feedback from the rotary encoder and linear drive serves as constraints for the registration pipeline. The captured frames are reconstructed into a complete model through a two-step Iterative Closest Point (ICP) procedure that eliminates the vertical drift and model collapse (often referred to as “telescoping”) common in unconstrained scanning. To evaluate system performance, a modular anthropometric benchmark object representing a human-sized target (1.6 m) was scanned. The reconstructed model was assessed in terms of surface coverage and volumetric fidelity relative to a CAD reference. The results demonstrate high sampling stability, achieving a mean surface density of $0.760{\mathrm{points}/\mathrm{mm}}^2$ on front-facing surfaces. Geometric deviation analysis revealed a mean signed error of −1.54 mm ($\sigma =$ 2.27 mm), corresponding to a relative volumetric error of approximately 0.096% over the full vertical span. These findings confirm that a single-sensor system, when guided by precise kinematics, can mitigate the non-linear bending and drift artefacts of handheld acquisition, providing an accessible yet rigorously accurate alternative to industrial multi-sensor systems. Full article

Due to its excellent high-temperature resistance and fatigue properties, 8Cr4Mo4V high-temperature bearing steel has become a critical material for aero-engine main shaft bearings. Consequently, the surface integrity of this material after grinding directly determines the service performance and fatigue life of the bearings. To address the lack of clarity regarding the correlation between grinding process parameters and defect characteristics under strong thermal–mechanical coupling, systematic grinding experiments were conducted in this study. Scanning Electron Microscopy (SEM) and white light interferometry were employed to observe the ground surface morphology and subsurface damage characteristics under various process parameters. On this basis, the influence of key parameters—such as wheel linear speed and grinding depth—on the formation and distribution of defects, including micro-cracks and material spalling, was analyzed in depth. Through qualitative and quantitative analyses, this study aims to elucidate the mechanisms by which grinding parameters affect machining defects, thereby providing experimental data and references for the optimization of the grinding process for this type of material. Full article

The increasing demand for sustainable agriculture necessitates the development of eco-friendly alternatives to chemical pesticides. This study reports the design and characterization of biodegradable fibrous mats for the delivery of Bacillus subtilis, a plant-beneficial biocontrol agent, using cellulose acetate (CA) scaffolds functionalized with chitooligosaccharides (COS). Electrospun CA fibers were coated by electrospraying with COS or COS/B. subtilis suspensions in a single-step process to produce open, porous biohybrid scaffolds. Scanning electron microscopy confirmed uniform fiber formation and successful deposition of COS and bacterial layers, while ATR-FTIR spectroscopy verified the chemical composition of the fibrous mats. Water contact angle measurements indicated a shift from hydrophobic to highly hydrophilic surfaces, enhancing microbial adhesion and moisture-mediated activation. Mechanical testing demonstrated that thin COS coatings slightly improved tensile strength without compromising flexibility. Viability assays confirmed that encapsulated B. subtilis remained viable and capable of sporulation, and dual-culture assays demonstrated effective inhibition of Alternaria solani, Fusarium avenaceum, and Rhizoctonia solani. These results indicate that the electrospun/electrosprayed CA/COS platform provides a protective, sustainable, and effective delivery system for biocontrol agents. This approach offers a promising strategy for reducing reliance on synthetic pesticides while maintaining crop protection efficacy. Full article

Dimensional verification of turbomachinery rotors requires traceable accuracy on functional data and dense coverage of freeform blades. This study quantifies the expanded measurement uncertainty (U95) for a centrifugal rotor inspected with a bridge-type CMM (Nikon Altera 10.10.8) and a structured-light scanner (ATOS Compact Scan 5M Rev.1), using repeated measurements in accordance with ISO 10360 and ISO 15530-3. The CMM achieved U95 ≈ 4–6 µm on bores, whereas optical scanning yielded U95 ≈ 12–18 µm on freeform blade regions. Cross-system results exhibited systematic offsets, indicating that the two methods are not directly interchangeable in absolute terms. Nevertheless, they are complementary: CMM ensures datum traceability, while optical scanning enables rapid full-field blade assessment, supporting uncertainty-aware hybrid inspection. Full article

Corneal nerves (CNs) are essential to maintain corneal epithelial integrity and ocular surface homeostasis. In vivo confocal microscopy (IVCM) enables the acquisition of high-resolution visualization of CNs, allowing visualization on a microscopic level. Traditionally, CN images must be analyzed by manual examination, which is time consuming and labor intensive. Artificial intelligence (AI) has facilitated reliable analysis of CN parameters, allowing for automatic and semiautomatic analysis of CNs. These include the identification, segmentation, and quantitative analysis of various CN parameters. This review summarizes the applications of AI-driven, automatic, and semiautomatic models in the CN analysis of IVCM images while also focusing on their diagnostic relevance in dry eye disease (DED) and neuropathic corneal pain (NCP). Recent advancements in AI have transformed IVCM image analysis by improving reproducibility and reducing operator dependency and time. The AI-based algorithm has been demonstrated to have good performance and sensitivity to identify and quantify the CN metrics. AI has also been utilized to improve the diagnostic accuracy of DED with IVCM scans, involving multiple portions of the CNs, such as the inferior whorl region. When employed with IVCM images of patients with NCP, AI-assisted identification of microneuromas and changes in CN metrics has provided an improvement in diagnostic accuracy. Despite promising advances and outcomes, the widespread implementation of these AI models in CN image analysis requires large-scale validation. Future integration of multimodal AI algorithms remains a promising endeavor to enhance diagnostic accuracy and disease stratification. Full article

(1) Background: We sought to explore the changes in the biomolecular profile and morphology of Pathogenic heat-resistant E. coli isolated from animal-based food. (2) Methods: Six strains underwent heat (60 °C for 6 min), ultrasound (US; 299 W), UVC (4950 mJ/cm²), and combined treatments (UVC+US and heat+UVC). Afterwards, biomolecular characterization across four spectral regions was evaluated by Fourier transform infrared (FT-IR) spectroscopy and analyzed by principal component analysis (PCA) for treated and non-treated strains (control group). These regions are fatty acids (3010–2800 cm⁻¹), proteins and peptides (1700–1200 cm⁻¹), carbohydrates (1200–900 cm⁻¹), and amide A (3280–3120 cm⁻¹). Additionally, treated and untreated strains were assessed for surface damage using scanning electron microscopy (SEM). (3) Results: Among all the regions studied, the amide A and fatty acids regions exhibited the most significant variations in absorbance for treated strains compared to the control. Treatments such as US, heat, and UVC+US tended to increase Principal Components (PCs) and, consequently, absorbance. On the other hand, UVC and heat+UVC showed the opposite trend in these regions. SEM images showed filamentous cells for strains treated with UVC and UVC+US, indicating that cells continued to replicate under these conditions. These results highlight how thermal and non-thermal treatments influence specific biomolecular and morphological regions of E. coli. The methodologies used provide reliable data for understanding stress responses, which can guide the development of more effective technologies for eliminating multi-resistant pathogens. Full article

Acrylate-based self-polishing copolymer antifouling paint particles (SPC-APPs) are persistent micropollutants that act as carriers for biocidal heavy metals, posing significant ecological hazards to aquatic ecosystems. Despite their toxicity, the occurrence, characterization, and metal-leaching risks of SPC-APPs in estuarine environments remain largely understudied. This study investigated the contamination characteristics of SPC-APPs in surface sediments from the Yangtze River Estuary, a hotspot of shipping activity. A multi-technique analytical protocol was employed, combining density separation with scanning electron microscopy–energy-dispersive spectroscopy (SEM-EDS), inductively coupled plasma mass spectrometry (ICP-MS), and pyrolysis–gas chromatography/mass spectrometry (Py-GC/MS) to characterize the morphology, quantify particle abundance, and assess the correlation between SPC-APPs and sedimentary heavy metals. SPC-APPs were ubiquitously detected across all sampling sites, with abundances ranging from (0.82 ± 0.15) × 10³ to (3.65 ± 0.42) × 10³ particles g⁻¹ dry sediment. A distinct distribution property (South Branch > North Branch > offshore shoal) was identified, primarily driven by shipping density and hydrodynamic sorting. Morphologically, particles exhibited irregular, abraded surfaces, with EDS confirming Cu (1.76~5.63 wt%) and Zn (0.27~3.65 wt%) as major metallic components. Py-GC/MS analysis identified specific mass fragments (m/z 41, 69, 87) as diagnostic markers. Strong positive correlations were observed between SPC-APP abundance and sediment Cu (r = 0.82, p < 0.01) and Zn (r = 0.76, p < 0.01) concentrations, indicating that these particles are a primary source of metal contamination. Ecological risk assessment based on sediment quality benchmarks showed that Cu in the South Branch reached 82~91% of the probable effect concentration (PEC), highlighting potential risks to benthic organisms. This study provides critical baseline data on the distribution and speciation of SPC-APPs, underscoring their role as vectors for toxic metals and the need for targeted pollution control in high-shipping-intensity estuarine regions. Full article

This study evaluated the effects of different provisional luting cement removal methods on the shear bond strength (SBS) of resin cement to dentin following immediate dentin sealing (IDS) performed with two adhesive systems. A total of 168 extracted, caries-free human third molars were used, of which 144 were allocated for SBS testing and 24 for scanning electron microscopy (SEM) analysis. Specimens were assigned according to the IDS protocol (no IDS, IDS with OptiBond FL, or IDS with G2-Bond), followed by provisional cementation using an eugenol-free temporary cement. Contaminated surfaces were subsequently cleaned with a hand scaler, aluminum oxide (Al₂O₃) air abrasion, or Katana Cleaner prior to final bonding with a dual-cure resin cement. SBS was measured after 24 h of water storage, and surface morphology was evaluated by SEM at 2500× magnification. IDS significantly increased SBS under uncontaminated conditions, with G2-Bond-based IDS exhibiting higher bond strength values than specimens without IDS. However, provisional cement contamination significantly reduced SBS regardless of the cleaning method applied, and none of the tested protocols fully restored the bond strength observed in uncontaminated IDS-treated dentin. SEM analysis revealed residual cement remnants and surface alterations after cleaning, even in specimens that appeared macroscopically clean. Within the limitations of this in vitro study, IDS enhances resin–dentin bonding when contamination is avoided; however, current mechanical and chemical cleaning methods are insufficient to completely recover bond strength compromised by provisional cement contamination, highlighting the importance of preventing contamination and preserving IDS layer integrity during indirect restorative procedures. Full article

This study investigates the synergistic and fineness-dependent modification of base asphalt using rice husk biochar (RHB) and styrene–butadiene–styrene (SBS), aiming to achieve the efficient utilization of agro-waste resources while markedly improving the high-temperature performance and durability of green pavement materials and sustainable transportation infrastructure. Through conventional performance tests, rheological measurements, and microstructural analyses, the performance behavior of RHB-SBS composite-modified asphalt and the interaction mechanisms between the modifiers were systematically examined. The results indicate that the fineness of RHB has a significant effect on the performance of the composite-modified asphalt, with 300 mesh identified as the optimal particle size that provides the best balance between high-temperature stiffness, low-temperature ductility, and storage stability. When the RHB fineness is fixed at 300 mesh, increasing the RHB content from 0 to 16 wt% markedly enhances the high-temperature performance of the composite asphalt, while its low-temperature performance slightly decreases. Scanning electron microscopy (SEM) analysis reveals that the porous structure and large specific surface area of RHB enable it to form a stable spatial network within the asphalt matrix, thereby improving high-temperature stability. Fourier-transform infrared spectroscopy (FTIR) results show that the incorporation of RHB alters the chemical structure of the asphalt and increases the degree of crosslinking, while thermogravimetry–differential scanning calorimetry (TG-DSC) analysis further confirms that the thermal stability of the composite-modified asphalt is significantly enhanced. Full article

The incorporation of carbon nanotubes (CNT) into cementitious composites has shown strong potential for enhancing mechanical performance. However, conventional dispersion methods, such as ultrasonication and chemical functionalization, are costly, complex, and difficult to scale for construction applications. This study introduces an alternative approach based on the in situ synthesis of CNT on active silica grains, which enables their direct incorporation into mortar formulations. The material was produced via chemical vapor deposition and characterized by scanning electron microscopy, thermogravimetric analysis, energy-dispersive spectroscopy, and Fourier-transform infrared spectroscopy. The resulting nanostructured active silica (NAS) exhibited high carbon content (80.7%) and a 1350% yield, confirming efficient nanotubular deposition. Residual oxygen (9.12%), Mg (0.75%), and Al (0.17%) indicated partial retention of catalytic species, while Fe–Co promoters with Mg–Al modifiers enabled a catalytically active surface favorable to CNT growth. Mortars incorporating NAS restored the flexural strength losses associated with cement replacement by silica, achieving values comparable to the reference mixture and outperforming the silica-only sample; compressive strength increased by ~16.5%. These results demonstrate that NAS promotes effective CNT dispersion at the composite scale without additional dispersion techniques, reduces process complexity, and adds value to commercial silica, providing a scalable route for developing nanostructured cementitious composites for construction applications. Full article

The dynamic development of laser therapy in dentistry is associated, among other factors, with the bactericidal effect of the energy emitted by laser devices. Therefore, they are also helpful for decontamination. They are increasingly used in the treatment of peri-implantitis, a bacterial inflammation of peri-implant tissues that is the most severe late complication of implantation and a potential cause of implant loss. Therefore, this study aimed to assess the safety of laser decontamination of the implant surface with respect to its effect on the integrity of the implant structure. In the present study, blocks of the titanium alloys Ti-6Al4V and Ti6Al7Nb were fabricated using electron-beam powder bed fusion and laser powder bed fusion, respectively. These alloys, commonly used in implantology, here in the form of Ti block scaffolds, have been exposed to an Er³⁺:YAG laser under various parameters (energy range of 50–320 mJ, exposure times of 20 or 30 s), and their effects have been further observed. To determine the changes induced by the laser, the following techniques were used: X-ray diffraction (XRD), Rietveld refinement method, scanning electron microscopy (SEM) with EDS (Energy-dispersive X-ray Spectroscopy), and thermography. The results show that the proposed Ti6Al4V and Ti6Al7Nb scaffolds can be exposed to an Er³⁺:YAG laser without damage when the power is limited to 0.5 W. Full article

To effectively prevent and control coal spontaneous combustion, a novel heat-sensitive hydrogel for mine fire prevention and extinguishment was developed using hydroxypropyl methylcellulose (HPMC) and the organic flame-retardant, sodium alginate (SA). The hydrogel was prepared through single-factor variable control and material compounding. First, the optimal formulation of the hydrogel was determined using analytical instruments and techniques, including a viscometer, vacuum drying oven, and the inverted test tube method. Subsequently, its microstructural characteristics were examined using scanning electron microscopy (SEM) and infrared spectroscopy (FTIR). Finally, a fire suppression test platform was established to perform comparative experiments, verifying the hydrogel’s fire prevention, extinguishing, and cooling performance. Experimental results demonstrated that the optimal hydrogel formulation consists of 2.5 wt% HPMC and 0.3 wt% SA. At this ratio, the hydrogel exhibits excellent fluidity and water retention, ensuring prolonged coverage and wetting of coal surfaces. The gel undergoes a sol–gel phase transition at 58 °C, enabling it to fill voids, bind and reinforce coal particles, and reduce exposed surface area. After drying, the hydrogel forms a uniformly smooth surface capable of both coating the coal body and encapsulating individual coal particles. Following the hydrogel treatment, the coal sample retains its original functional groups, indicating that no chemical reactions occur during mixing. Compared with traditional inhibitors, the hydrogel demonstrates superior fire suppression performance, more effectively covering and encapsulating burning coal. It rapidly reduces the temperature to 28 °C by the cooling effect of water evaporation from the hydrogel, and it maintains thermal stability, achieving outstanding fire-extinguishing efficiency. Full article

Additive manufacturing is increasingly used in construction, yet reliable quality assurance for three-dimensional-printed concrete elements remains a major challenge. Existing digital defect-detection methods, particularly voxel-based and mesh-based approaches, are often evaluated separately, which limits understanding of their relative capabilities for construction-scale inspection. This study establishes a controlled comparison of the two representations using identical scan-to-design data, consistent preprocessing, and unified defect thresholding. A voxel pipeline employing signed distance fields and a three-dimensional convolutional neural network, and a mesh pipeline using triangular surface reconstruction, geometric surface descriptors, and MeshCNN, were applied to structured-light scans of printed clay wall segments containing intentional voids, material buildup, and layer-height inconsistencies. Across common performance metrics, the voxel-based method achieved a recall of 95% for spatially coherent, volumetric-consistent void-related anomalies inferred from surface geometry, reflecting improved aggregation of distributed deviations, while the mesh-based method attained a mean surface defect localization error of 0.32 mm with a substantially lower computational cost in runtime and memory. These results clarify representation-dependent trade-offs and provide guidance for selecting appropriate inspection pipelines in extrusion-based construction. The findings establish a controlled, construction-oriented comparative framework for digital defect detection and support more efficient, reliable, and scalable quality-assurance workflows for sustainable additive manufacturing. Full article

The corrosion behavior and passive-film stability of a β-TiZrNbTa (β-TZNT) alloy were investigated in artificial seawater (ASW), focusing on the effects of pH, temperature, immersion time, fluoride ion concentration, and potential scan rate. In addition to electrochemical methods such as open-circuit potential (OCP), potentiodynamic polarization (PDP), and electrochemical impedance spectroscopy (EIS), scanning electron microscopy (SEM) and X-ray diffraction (XRD) were employed for surface characterization. The establishment of a stable and efficient passive layer enriched in Zr-, Nb-, and Ta-oxides was responsible for the β-TZNT alloy’s superior corrosion resistance in fluoride-free ASW when compared to commercially pure titanium. Reduced passive-film resistance resulted from corrosion kinetics being greatly accelerated by decreasing the pH and increasing the temperature. The presence of fluoride ions strongly affected the passivity of the alloy due to the chemical dissolution of TiO₂ through the formation of soluble fluoride complexes, resulting in an increase in the corrosion current densities by more than one order of magnitude. A bilayer passive structure with a compact inner barrier layer and a porous outer layer was identified by EIS analysis. The stability of this structure gradually decreased with increasing fluoride concentration and acidity. Over time, passive-film degradation was dominant in fluoride-free seawater, whereas prolonged exposure in fluoride-containing media promoted partial re-passivation. Overall, these results highlight the potential and limitations of the β-TZNT alloy for marine and offshore applications by offering new mechanistic insights into the synergistic effects of fluoride ions and environmental factors on corrosion performance. Full article

Laser surface texturing (LST) structures or laser-induced periodic surface structures (LIPSS) are typically created using laser pulses with durations ranging from femtoseconds to nanoseconds. However, nanosecond pulsed lasers, as cost-effective and more productive alternatives, can also be used to generate LST structures on stainless steel (SS) surfaces, making these structures more suitable for industrial applications. In this study, pulsed laser processing is employed to create LST structures on SS (AISI 304), with varying pulse and accumulated fluences, effective pulse counts, and scan parameters, such as pulse-to-pulse distance (pitch) and hatch spacing between scanning lines. A methodology for calculating oxidation density on processed AISI 304 surfaces is presented. Oxidation density, defined as the ratio of the oxidized area to the total processed area, is determined as a function of accumulated fluence, laser power, pulse-to-pulse distance, and hatch spacing. Optical images of the surfaces are analyzed, and oxidation regions are identified using machine learning techniques. The images are converted to grayscale, and machine learning algorithms are applied to classify the images into oxidation and non-oxidation regions based on pixel intensity values. This approach identifies the optimal threshold for separating the two regions by maximizing inter-class variance. Experimental modeling using response surface methodology is applied to experimentally generated data. Optimization algorithms are then employed to determine the process parameters that maximize pulsed laser irradiation performance while minimizing surface oxidation and processing time. This paper also presents a novel method for characterizing oxidation density using image segmentation and machine learning. The results provide a comprehensive understanding of the process and offer optimized models, contributing valuable insights for practical applications. Full article