Search Results (10,096)

Objective: Several studies have compared the accuracy of digital scans and conventional impressions for post space capture. However, only a few have specifically investigated the precision of intraoral scanners in measuring post spaces of varied lengths. This study aimed to evaluate the accuracy of various intraoral scanning techniques in capturing long and short post spaces. Material and Methods: This study grouped samples into eight categories based on four techniques and two post space depths (7 and 11 mm). After tooth preparation, root canal treatment, and post space preparation, laboratory scans were performed using Duralay. Intraoral scans were obtained directly and indirectly with the Trios fourth generation scanner using the Duralay and PVS techniques. The accuracies, in terms of trueness, and precisions were compared after ten repetitions for each group using the Kruskal–Wallis or Mann–Whitney U tests. Results: The Duralay Intraoral Scan groups demonstrated a high consistency, while the Direct Intraoral Scan groups showed moderate consistency. Variability was higher for the Duralay Lab Scan and PVS Intraoral Scan groups for short post spaces. Conclusions: The capture technique significantly affected the accuracies of the post space measurements. The techniques also demonstrated varying consistency and precision. These findings provide critical insights to guide their selections for clinical and research applications. Clinical Significance: This study is one of the few to compare the accuracy of intraoral scanners for the capture of both short and long post spaces. It addresses a key gap in current dental research and offers practical guidance for clinicians and researchers in selecting appropriate scanning techniques for various clinical scenarios. The findings have the potential to enhance the accuracy and reliability of post space measurements and improve patient outcomes. Full article

(1) Background: This study aimed to evaluate the marginal and internal fit of ceramic crowns produced by various digital methods using microcomputed tomography (MCT) imaging. (2) Methods: The ceramic crown preparation was performed on typodont maxillary first premolar. The crown preparation was scanned with an intraoral scanner and a model scanner, and cone-beam computed tomography (CBCT) scans were performed with three different voxel sizes (0.075 mm, 0.1 mm, and 0.15 mm). The space between the crown and prepared teeth was measured at nine different points in both coronal and sagittal sections. Three different digital model acquisition techniques, namely, intraoral scanning, model scanning, and CBCT-based standard tessellation language (STL) reconstruction, were compared in terms of marginal and internal fit. (3) Results: Quantitative analyses revealed that model scanners exhibited the lowest marginal and internal gap values, indicating superior fit compared to intraoral scanners and CBCT-based models. The highest gap values were observed in the CBCT group with a voxel size of 0.15 mm. Overall, crowns obtained from model scanners demonstrated the highest success rates in both marginal and internal fit. (4) Conclusions: In conclusion, this study highlights the critical role of digital scanning accuracy in achieving clinically acceptable prosthetic fits and emphasizes the need for continued technological advancement. Full article

Pedicle screw fixation is a common spinal surgery technique, but concerns remain about stability when screws are malpositioned. Traditional in vitro pull-out tests assess anchorage but lack physiological accuracy. This study examined the stability of correctly placed and intentionally malpositioned pedicle screws on forty vertebrae from five cadavers. Optimal screw paths were planned via CT scans and applied using 3D-printed guides. Four malposition types—medial, lateral, superior, and superior-lateral—were created by shifting the original trajectory. A custom setup applied three consecutive cycles of tensile and compressive load from 50 N to 200 N. Screw inclination under load was measured with a 3D optical system. The results showed increasing screw inclination with higher forces, reaching about 1° at 50 N and 2° at 100 N, similar in both load directions. Significant differences in inclination were only found at 100 N tensile load, where malpositioned screws showed a lower inclination. Overall, malpositioning had no major effect on screw loosening. These findings suggest that minor deviations in screw placement do not significantly compromise mechanical stability. Clinically, the main concern with malpositioning lies in the potential for injury to nearby structures rather than reduced screw fixation strength. Full article

The growing global demand for energy necessitates the efficient utilization of unconventional petroleum resources, particularly heavy oil reserves. However, extracting, transporting, and processing these resources remain challenging due to their low mobility, low API gravity, and significant concentrations of resins, asphaltenes, heteroatoms, and metals. In recent years, various in situ upgrading techniques have been explored to enhance heavy oil quality, with catalytic aquathermolysis emerging as a promising approach. The effectiveness of this process largely depends on the development of cost-effective, environmentally friendly catalysts. This study investigates the upgrading performance of water-soluble ammonium alum, (NH₄)Al(SO₄)₂·12H₂O, for an extra-heavy oil sample from the Zarafshan Depression, located along the Tajikistan–Uzbekistan border. Comprehensive analyses demonstrate that the catalyst facilitates the breakdown of heavy oil components, particularly resins and asphaltenes, into lighter fractions. As a result, oil viscosity was significantly reduced by 94%, while sulfur content decreased from 896 ppm to 312 ppm. Furthermore, thermogravimetric (TG-DTG) analysis, coupled with Fourier-transform infrared spectroscopy (FT-IR), scanning electron microscopy (SEM), and X-ray diffraction (XRD), revealed that the thermal decomposition of ammonium alum produces catalytically active Al₂O₃ nanoparticles. These findings suggest that ammonium alum is a highly effective water-soluble pre-catalyst for hydrothermal upgrading, offering a viable and sustainable solution for the development of extra-heavy oil fields. Full article

Background and Objectives: Three-dimensional (3D) scanning is increasingly utilized in medical practice, from orthotics to surgical planning. However, traditional hand measurement techniques remain inconsistent and prone to human error and are often time-consuming. This research evaluates the practicality of a commercial 3D scanning method by comparing the accuracy of scans conducted by two user groups. Materials and Methods: This study evaluated the following two groups: an experimental group (n = 45) and a control group (n = 42). A total of 261 hand scans were captured using the Structure Sensor Pro 3D scanner for iPad (Structure, Boulder, CO, USA). The scans were then evaluated using Meshmixer software (version 3.5.474), analyzing key parameters, such as surface area, volume, number of vertices, and triangles, etc. Furthermore, a digital literacy test and a user experience survey were conducted to support a more comprehensive evaluation of participant performance within the study. Results: The experimental group outperformed the control group on all measured parameters, including surface area, volume, vertices, triangle, and gap count, with large effect sizes observed. User experience data revealed that participants in the experimental group rated the 3D scanner significantly higher across all dimensions, particularly in ease of use, excitement, supportiveness, and practicality. Conclusions: A short 15 min training session can promote scan reliability, demonstrating that even minimal instruction improves users’ proficiency in 3D scanning, fundamental for supporting clinical accuracy in diagnosis, surgical planning, and personalized device manufacturing Full article

Aluminum nitride (AlN) thin films were deposited on SiO₂ substrates by RF-magnetron sputtering at varying powers (110–140 W) and subsequently subjected to thermal annealing at 450 °C under nitrogen atmosphere. A comprehensive multi-technique investigation—including X-ray reflectometry (XRR), X-ray diffraction (XRD), scanning electron microscopy (SEM), atomic force microscopy (AFM), optical profilometry, spectroscopic ellipsometry (SE), and electrical measurements—was performed to explore the physical structure, morphology, and optical and electrical properties of the films. The analysis of the film structure by XRR revealed that increasing sputtering power resulted in thicker, denser AlN layers, while thermal treatment promoted densification by reducing density gradients but also induced surface roughening and the formation of island-like morphologies. Optical studies confirmed excellent transparency (>80% transmittance in the near-infrared region) and demonstrated the tunability of the refractive index with sputtering power, critical for optoelectronic applications. The electrical characterization of Au/AlN/Al sandwich structures revealed a transition from Ohmic to trap-controlled space charge limited current (SCLC) behavior under forward bias—a transport mechanism frequently present in a material with very low mobility, such as AlN—while Schottky conduction dominated under reverse bias. The systematic correlation between deposition parameters, thermal treatment, and the resulting physical properties offers valuable pathways to engineer AlN thin films for next-generation optoelectronic and high-frequency device applications. Full article

This study investigates the impact of mineralogical and petrographic characteristics on the mechanical behavior of granitic and mafic rocks from the Shuangjiangkou (Sichuan Province) and Damiao complexes (Hebei Province) in China. The research methodology combined petrographic investigation, comprising optical microscopy and Scanning Electron Microscopy–Energy-Dispersive X-ray Spectroscopy (SEM-EDS) methods, with methodical geotechnical characterization to establish quantitative relationships between mineralogical composition and engineering properties. The petrographic studies revealed three lithologic groups: fine-to-medium-grained Shuangjiangkou granite (45%–60% feldspar, 27%–35% quartz, 10%–15% mica), plagioclase-rich anorthosite (more than 90% of plagioclase), and intermediate mangerite (40%–50% of plagioclase, 25%–35% of perthite). The uniaxial compressive strength tests showed great variations: granite (127.53 ± 15.07 MPa), anorthosite (167.81 ± 23.45 MPa), and mangerite (205.12 ± 23.87 MPa). Physical properties demonstrated inverse correlations between mechanical strength and both water absorption (granite: 0.25%–0.42%; anorthosite: 0.07%–0.44%; mangerite: 0.10%–0.25%) and apparent porosity (granite: 0.75%–0.92%; anorthosite: 0.20%–1.20%; mangerite: 0.29%–0.69%), with positive correlations to specific gravity (granite: 1.88–3.03; anorthosite: 2.67–2.90; mangerite: 2.43–2.99). Critical petrographic features controlling mechanical behavior include the following: (1) mica content in granite creating anisotropic properties, (2) extensive feldspar alteration through sericitization increasing microporosity and reducing intergranular cohesion, (3) plagioclase micro-fracturing and alteration to clinozoisite–sericite assemblages in anorthosite creating weakness networks, and (4) mangerite’s superior composition of >95% hard minerals with minimal sheet mineral content and limited alteration. Failure mode analysis indicated distinct patterns: granite experiencing shear-dominated failure (30–45° diagonal planes), anorthosite demonstrated tensile fracturing with vertical splitting, and mangerite showed catastrophic brittle failure with extensive fracture networks. These findings provide quantitative frameworks that relate petrographic features to engineering behavior, offering valuable insights for rock mass assessment and engineering design in similar crystalline rock terrains. Full article

Three-dimensional (3D) reproduction of artworks has advanced significantly, offering valuable insights for conservation by documenting the objects’ conservative state at both macroscopic and microscopic scales. This paper presents the 3D survey of an earthquake-damaged panel painting, whose wooden support suffered severe deformation during a seismic event, posing unique restoration challenges. Our work focuses on quantifying how shape variations in the support—induced during restoration—affect the surface morphology of the pictorial layers. To this end, we conducted measurements before and after support consolidation using two complementary 3D techniques: structured-light projection to generate 3D models of the painting, tracking global shape changes in the panel, and laser-scanning microprofilometry to produce high-resolution models of localized areas, capturing surface morphology, superficial cracks, and pictorial detachments. By processing and cross-comparing 3D point cloud data from both techniques, we quantified shape variations and evaluated their impact on the pictorial layers. This approach demonstrates the utility of multi-scale 3D documentation in guiding complex restoration interventions. Full article

Quantum memory is essential for the prolonged storage and retrieval of quantum information. Nevertheless, no current studies have focused on the creation of effective quantum memory for continuous variables while accounting for the decoherence rate. This work presents an effective continuous-variable quantum key distribution method with parameter optimization utilizing the Elitist Elk Herd Random Immigrants Optimizer (2E-HRIO) technique. At the outset of transmission, the quantum device undergoes initialization and authentication via Compressed Hash-based Message Authentication Code with Encoded Post-Quantum Hash (CHMAC-EPQH). The settings are subsequently optimized from the authenticated device via 2E-HRIO, which mitigates the effects of decoherence by adaptively tuning system parameters. Subsequently, quantum bits are produced from the verified device, and pilot insertion is executed within the quantum bits. The pilot-inserted signal is thereafter subjected to pulse shaping using a Gaussian filter. The pulse-shaped signal undergoes modulation. Authenticated post-modulation, the prediction of link failure is conducted through an authenticated channel using Radial Density-Based Spatial Clustering of Applications with Noise. Subsequently, transmission occurs via a non-failure connection. The receiver performs channel equalization on the received signal with Recursive Regularized Least Mean Squares. Subsequently, a dataset for side-channel attack authentication is gathered and preprocessed, followed by feature extraction and classification using Adaptive Depthwise Separable Convolutional Neural Networks (ADS-CNNs), which enhances security against side-channel attacks. The quantum state is evaluated based on the signal received, and raw data are collected. Thereafter, a connection is established between the transmitter and receiver. Both the transmitter and receiver perform the scanning process. Thereafter, the calculation and correction of the error rate are performed based on the sifting results. Ultimately, privacy amplification and key authentication are performed using the repaired key via B-CHMAC-EPQH. The proposed system demonstrated improved resistance to decoherence and side-channel attacks, while achieving a reconciliation efficiency above 90% and increased key generation rate. Full article

Ceramic dental prosthetics with internal metal structures are made from a cobalt–chromium alloy that is coated with ceramic. This study aims to validate surface treatments for the metal that enhance the adhesion of the ceramic coating under masticatory forces. Surface conditioning is performed using mechanical methods, like sandblasting (SB), and thermal methods, such as oxidation (O). The ceramic coating is applied to the metal component following the conditioning process, which can be conducted using either a single method or a combination of methods. Each conditioned sample undergoes characterization through various techniques, including drop shape analysis (DSA), scanning electron microscopy (SEM), X-ray diffraction (EDX), and atomic force microscopy (AFM). After the ceramic coating is applied and subjected to thermal sintering, the metal–ceramic samples are mechanically tested to assess the adhesion of the ceramic layer. The research findings, illustrated by scanning electron microscopy (SEM) images of the metal structures’ surfaces, indicate that alloy powder particles ranging from 10 to 50 µm were either adhered to the surfaces or present as discrete dots. Particles that exceed the initial design specifications of the structure can be smoothed out using sandblasting or mechanical finishing techniques. The energy-dispersive spectroscopy (EDS) results show that, after sandblasting, fragments of aluminum oxide remain trapped on the surface of the metal structures. These remnants are considered impurities, which can negatively impact the adhesion of the ceramic to the metal substrate. The analysis focuses on the exfoliation of the ceramic material from the deformed metal surfaces. The results emphasize the significant role of the sandblasting method and the micro-topography it creates, as well as the importance of the oxidation temperature in the treatment process. Drawing on 25 years of experience in dental prosthetics and the findings from this study, this publication aims to serve as a guide for applying the ceramic bonding layer to metal surfaces and for conditioning methods. These practices are essential for enhancing the adhesion of ceramic materials to metal substrates. Full article

Background: The accurate prediction of pathological complete response (pCR) following total neoadjuvant therapy (TNT) is crucial for optimising treatment protocols in locally advanced rectal cancer (LARC). Although conventional imaging techniques such as MRI show limitations in assessing treatment response, metabolic imaging utilising 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET-CT) provides distinctive information by quantifying tumour glycolytic activity. This study investigates the predictive value of sequential 18F-FDG PET-CT parameters, focusing on Total Lesion Glycolysis (TLG), in predicting pCR after TNT. Methods: We conducted a retrospective analysis of 33 LARC patients (T3–4/N0–1) treated with TNT (neoadjuvant-chemoradiation followed by consolidation FOLFOX chemotherapy). Sequential PET-CT scans were performed at baseline, interim (after 4 cycles of FOLFOX), and post-TNT. Metabolic parameters, including maximum standardised uptake value (SUVmax) and TLG, were measured. Receiver operating characteristic (ROC) analysis assessed the predictive performance of these parameters for pCR. Results: The pCR rate was 21.2% (7/33). Post-TNT TLG ≤ 10 demonstrated excellent predictive accuracy for pCR (AUC 0.887, 92.3% sensitivity, 85.7% specificity, and 96.0% PPV), outperforming SUVmax (AUC 0.843). Interim TLG ≤ 10 also showed a strong predictive value (AUC 0.824, 100% sensitivity, and 71.4% specificity). Conclusions: TLG may serve as a reliable metabolic biomarker for predicting pathologic complete response (pCR) after total neoadjuvant therapy (TNT) in locally advanced rectal cancer (LARC). Its inclusion in clinical decision-making could improve patient selection for organ preservation strategies, thereby reducing the need for unnecessary surgeries in the future. However, given that the study is based on a small retrospective design, the findings should be interpreted with caution and used alongside other decision-making tools until more comprehensive data are collected from larger studies. Full article

This study investigates the development of a fully rubberized fine-aggregate engineered geopolymer composite (R-EGC) by replacing quartz sand with waste rubber particles (RPs). The influence of the rubber aggregate-to-binder mass ratio (A/B) on the performance of the R-EGC was systematically examined from both macroscopic and microscopic perspectives. Quantitative analysis of crack width and number was conducted using binarized image-processing techniques to elucidate the crack propagation patterns. Moreover, scanning electron microscopy (SEM) and energy-dispersive spectroscopy (EDS) were employed to analyze the interfacial transition zone (ITZ) between the rubber aggregates and the geopolymer matrix under varying A/B ratios, aiming to explore the underlying failure mechanisms of the R-EGC. The research results indicated that the flowability of the R-EGC decreased gradually with increasing A/B ratio. The flowability of R-0.1 was 73.5%, outperforming R-0.2 and R-0.3 (66% and 65%, respectively). R-0.1 achieved the highest compressive strength of 35.3 MPa (compared to 31.2 MPa and 28.4 MPa for R-0.2 and R-0.3, respectively). R-0.3 demonstrated the most effective crack-control capability, with a tensile strength of 3.96 MPa (representing increases of 11.9% and 3.7% compared to R-0.1 and R-0.2, respectively) and the smallest crack width of 104 μm (indicating reductions of 20.6% and 43.5% compared to R-0.1 and R-0.2, respectively). R-0.2 exhibited the best ductility, with an ultimate tensile strain of 8.33%. Microstructural tests revealed that the interfacial transition zone (ITZ) widths for R-0.1, R-0.2, and R-0.3 were 2.47 μm, 4.53 μm, and 1.09 μm, respectively. An appropriate increase in the ITZ width was found to be beneficial for enhancing tensile ductility, but it compromised the crack-control ability of the R-EGC, thereby reducing its durability. Overall, this study clarifies the fundamental influence of the A/B ratio on the mechanical performance of the R-EGC. The findings provide valuable insights for future research in this field. Full article

This paper addresses the issues of insufficient expansion force, low early strength (1-day compressive strength < 1.5 MPa), and poor toughness (flexural strength < 0.8 MPa) in traditional chemical foamed cement used for road grouting repair. By combining single-factor gradient experiments with microscopic mechanism analysis, the study systematically investigates the performance modulation mechanisms of controlled-release foamed cement using additives such as heavy calcium powder (0–20%), calcium chloride (0.2–1.2%), latex powder (0.2–1.2%), and polypropylene fiber (0.2–0.8%). The study innovatively employs a titanium silicate coupling agent coating technique (with the coating agent amounting to 25% of the catalyst’s mass) to delay foaming by 40 s. Scanning electron microscopy (SEM) and pore structure analysis reveal the microscopic essence of material performance optimization. Full article

Background: The ability of Graph Neural Networks (GNNs) to analyse brain structural patterns in various kinds of neurodegenerative diseases, including Parkinson’s disease (PD), has drawn a lot of interest recently. One emerging technique in this field is brain age prediction, which estimates biological age to identify ageing patterns that may serve as biomarkers for such disorders. However, a significant problem with most of the GNNs is their depth, which can lead to issues like oversmoothing and diminishing gradients. Methods: In this study, we propose SAGEFusionNet, a GNN architecture specifically designed to enhance brain age prediction and assess PD-related brain ageing patterns using T1-weighted structural MRI (sMRI). SAGEFusionNet learns important ROIs for brain age prediction by incorporating ROI-aware pooling at every layer to overcome the above challenges. Additionally, it incorporates multi-layer feature fusion to capture multi-scale structural information across the network hierarchy and auxiliary supervision to enhance gradient flow and feature learning at multiple depths. The dataset utilised in this study was sourced from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. It included a total of 580 T1-weighted sMRI scans from healthy individuals. The brain sMRI scans were parcellated into 56 regions of interest (ROIs) using the LPBA40 brain atlas in CAT12. The anatomical graph was constructed based on grey matter (GM) volume features. This graph served as input to the GNN models, along with GM and white matter (WM) volume as node features. All models were trained using 5-fold cross-validation to predict brain age and subsequently tested for performance evaluation. Results: The proposed framework achieved a mean absolute error (MAE) of $4.24\pm 0.38$ years and a mean Pearson’s Correlation Coefficient (PCC) of $0.72\pm 0.03$ during cross-validation. We also used 215 PD patient scans from the Parkinson’s Progression Markers Initiative (PPMI) database to assess the model’s performance and validate it. The initial findings revealed that out of 215 individuals with Parkinson’s disease, 213 showed higher and 2 showed lower predicted brain ages than their actual ages, with a mean MAE of 13.36 years (95% confidence interval: 12.51–14.28). Conclusions: These results suggest that brain age prediction using the proposed method may provide important insights into neurodegenerative diseases. Full article

This study presents a comprehensive methodology for the detection and characterization of fractures in geological samples using X-ray computed tomography (CT). By combining convolution-based image processing techniques with advanced neural network-based segmentation, the proposed approach achieves high precision in identifying complex fracture networks. The method was applied to a marly limestone sample from the Maiolica Formation, part of the Umbria–Marche stratigraphic succession (Northern Apennines, Italy), a geological context where fractures often vary in size and contrast and are frequently filled with minerals such as calcite or clays, making their detection challenging. A critical part of the work involved addressing multiple sources of uncertainty that can impact fracture identification and measurement. These included the inherent spatial resolution limit of the CT system (voxel size of 70.69 μm), low contrast between fractures and the surrounding matrix, artifacts introduced by the tomographic reconstruction process (specifically the Radon transform), and noise from both the imaging system and environmental factors. To mitigate these challenges, we employed a series of preprocessing steps such as Gaussian and median filtering to enhance image quality and reduce noise, scanning from multiple angles to improve data redundancy, and intensity normalization to compensate for shading artifacts. The neural network segmentation demonstrated superior capability in distinguishing fractures filled with various materials from the host rock, overcoming the limitations observed in traditional convolution-based methods. Overall, this integrated workflow significantly improves the reliability and accuracy of fracture quantification in CT data, providing a robust and reproducible framework for the analysis of discontinuities in heterogeneous and complex geological materials. Full article