Search Results (3,714)

Background: The present article describes a step-by-step maximally digitalized workflow protocol with computer-aided design and computer-aided manufacturing (CAD/CAM) in partial-arch edentulous patients rehabilitated with fixed dental prostheses and removable partial dentures (FDPs and RPDs). Methods: Facial digitalization, intraoral scans, and functional mandibular movement recordings were used to create a 4D virtual patient on commercially available CAD software. The fixed components including post-and-cores, both metal–ceramic with extra-coronal attachment and monolithic zirconia crowns, and the RPDs were manufactured by computer numerical controlled direct milling. Results: This innovative digital approach using the virtual patient and the superimposition of interim RPDs fitted in the mouth has been used to provide fixed and removable rehabilitation to the patient without clinical complications with 2 years of follow-up. Conclusions: Within the limitations of this report, the developed combined prosthesis fabrication technique allowed optimization of the production by decreasing the clinical steps and laboratory procedures in partial-arch edentulous rehabilitated with FDPs and RPDs. Full article

The combination of highly dynamic systems with a limited work envelope with a less dynamic system with a larger working envelope promises to combine the advantages of both systems while eliminating the disadvantages. For these systems, separation algorithms determine the trajectories based on the target geometries. However, arbitrary processing orders of these result in inefficient trajectories because successive geometries may be geometrically far apart. This causes the dynamic system to operate below its potential. Current planning tools do not optimise the processing order for such redundant systems. The aim is to design and implement a planning tool for the application of laser marking. The tool considers the processing order of the 2D geometries from a geometric point of view. The resulting sequenced path data can then be used by trajectory generation algorithms to make full use of the potential of redundant systems. The approach analyses literature on Travelling Salesman Problems (TSP), which is then transferred to the given application. A heuristic and a genetic algorithm are developed and integrated into a planning tool. The results show the heuristic algorithm being faster while still producing solutions whose total path length is similar to that of the genetic algorithm. Even though the solutions don’t meet any optimality standards, the presented automated approaches are superior to manual approaches and are to be seen as a starting point for further research. Full article

The shape design and optimization of complex disk curves is a crucial and intractable technique in computer-aided design and manufacturing (CAD/CAM). Based on disk Wang–Ball (DWB) curves, this paper defines a novel combined disk Wang–Ball (CDWB) curve with constrained parameters and investigates the shape optimization of CDWB curves by using the multi-strategy ameliorated chameleon swarm algorithm (MCSA). Firstly, in order to meet the various shape design requirements, the CDWB curves consisting of n DWB curves are defined, and the G¹ and G² geometric continuity conditions for the curves are derived. Secondly, the shape optimization of CDWB curves is considered as a minimization problem with curve energy as the objective, and an optimization model is developed under the constraints of the splicing conditions. Finally, the meta-heuristic algorithm MCSA is introduced to solve the established optimization model to obtain the minimum energy value, and its performance is verified by comparison with other algorithms. The results of representative numerical examples confirm the effectiveness and competitiveness of the MCSA for the CDWB curve shape optimization problems. Full article

Cemented paste backfill (CPB) is a cemented void filling method gaining popularity over traditional hydraulic or rockfill methods. As mining depth increases, CPB-filled stopes are subjected to higher confining pressures. Due to the soil triaxial apparatus limitations, as the conventional method of triaxial testing on CPB, no confining pressures higher than 5 MPa can be applied to CPB over a range of curing time. This lack of data introduces uncertainty in predicting CPB behavior, potentially leading to an overestimation of the required strength. To address this, this study introduces a new testing method that allows for higher confinement beyond traditional limitations by modifying the Hoek triaxial cell to accommodate low-strength materials. This study then investigates the coupled influence of confining pressure and curing time (hydration) on CPB characteristics, specifically examining the impacts of different curing times and confining pressures on the mechanical and rheological properties of CPB. A total of 75 triaxial tests were conducted using 42 mm cylinder shape samples at five various curing times from 7 to 96 days, and applied at low and high confinement condition levels (0.5 to 30 MPa). The results reveal that hydration and confinement positively impact the CPB strength. The modified structured Cam-Clay model was selected to predict the behavior, and its yield surface was updated using the experimental results. The proposed yield model can be utilized to describe CPB material subjected to various curing and pressure conditions underground. Full article

Background/Objectives: Mandibular gingival squamous cell carcinoma (SCC) is the second most common oral cancer after tongue cancer. As these carcinomas often invade the mandible early, accurately defining the resection extent is important. This report highlights the use of preoperative virtual surgery data, computer-aided design and manufacturing (CAD/CAM) technology, surgical guidance, and extended reality (XR) support in achieving highly accurate marginal mandibulectomy without recurrence or metastasis. Methods: CT imaging data obtained a month before surgery were imported into Materialize Mimics and Materialize Magics (Materialize, Leuven, Belgium, Ver22.0) CAD/CAM software and used to design an osteotomy guide. An STL file was generated, and the guide was fabricated using a 3D printer (Objet 260 Connex; Stratasys Ltd., Eden Prairie, MN, USA) prior to the operation. An XR application, installed on a HoloLens (Microsoft, WA, USA) head-mounted display, projected a hologram onto the surgical field. Results: The rapid intraoperative diagnostic tests were negative, and histopathology confirmed SCC without vascular or perineural invasion. No complications, including occlusal or feeding problems and sensory abnormalities, were observed. Postoperative imaging 3 years later showed no recurrence. Conclusions: Combining CAD/CAM and XR techniques for mandibulectomy may improve surgical accuracy and safety in oral and maxillofacial surgeries, whereas in-house 3D printing aids in managing tumor progression. Full article

Objectives: Among modern metal-free materials, zirconia, a high-performance ceramic material that can only be manufactured through CAM procedures, has certainly exponentially gained popularity thanks to its mechanical strength, biocompatibility, esthetic, and versatility. However, one of the main debates that has been raised in relation to zirconia is its usage as an adhesive material. The present case report describes the clinical outcome of a multidisciplinary case finalized with adhesive minimally invasive zirconia veneers for the treatment of discolored teeth after a 24-month follow-up. Methods: A 19-year-old female patient with discolored upper frontal teeth (first premolar to first premolar) negatively affecting her self-esteem and social life was visited by a prosthodontic specialist. The treatment plan included orthodontic treatment, soft and hard tissue management through surgical procedures, and, lastly, minimally invasive adhesive zirconia veneers. The zirconia veneers bonding was performed under a rubber dam by conditioning the dental substrate by sandblasting the enamel with 40-micron aluminum oxide, etching with orthophosphoric acid 37%, and using a proper adhesive system. Monolithic zirconia restorations were sandblasted with 70-micron aluminum oxide at 0.2 MPa, then cleaned with a specific cleaner, and treated with a primer. Results: At the last follow-up (24 months), neither biological nor mechanical complications were observed. The patient anecdotally reported being very satisfied with the functional and esthetic results obtained. Therefore, the case was considered successful. Conclusions: Within the limitations of the present case report, the reported case on the use of minimally invasive resin-bonded zirconia veneers for the treatment of discolored teeth showed excellent outcomes after a 24-month follow-up. The use of zirconia as an adhesive material seems to be emerging. However, more clinical studies are required to validate the procedure. Full article

Background and Objectives: Computer-aided diagnostic systems have achieved remarkable success in the medical field, particularly in diagnosing malignant tumors, and have done so at a rapid pace. However, the generalizability of the results remains a challenge for researchers and decreases the credibility of these models, which represents a point of criticism by physicians and specialists, especially given the sensitivity of the field. This study proposes a novel model based on deep learning to enhance lung cancer diagnosis quality, understandability, and generalizability. Methods: The proposed approach uses five computed tomography (CT) datasets to assess diversity and heterogeneity. Moreover, the mixup augmentation technique was adopted to facilitate the reliance on salient characteristics by combining features and CT scan labels from datasets to reduce their biases and subjectivity, thus improving the model’s generalization ability and enhancing its robustness. Curriculum learning was used to train the model, starting with simple sets to learn complicated ones quickly. Results: The proposed approach achieved promising results, with an accuracy of 99.38%; precision, specificity, and area under the curve (AUC) of 100%; sensitivity of 98.76%; and F1-score of 99.37%. Additionally, it scored a 00% false positive rate and only a 1.23% false negative rate. An external dataset was used to further validate the proposed method’s effectiveness. The proposed approach achieved optimal results of 100% in all metrics, with 00% false positive and false negative rates. Finally, explainable artificial intelligence (XAI) using Gradient-weighted Class Activation Mapping (Grad-CAM) was employed to better understand the model. Conclusions: This research proposes a robust and interpretable model for lung cancer diagnostics with improved generalizability and validity. Incorporating mixup and curriculum training supported by several datasets underlines its promise for employment as a diagnostic device in the medical industry. Full article

Background: This study aims to perform a review by selecting, analyzing, and evaluating articles that discuss the accuracy of intraoral scanners (IOSs) in recording post space compared to conventional impression-making techniques. Methods: The review question framed using the PITR framework (participant, index test, targeted condition, and reference standard) is as follows: What is the dimensional accuracy (T) of impressions made using intraoral scanners (I) for post space (P) compared to impressions made using conventional techniques and digitalized using extraoral scanners (R)? Four electronic databases were searched using pre-set keywords. The guidelines and strategies recommended by PRISMA formed the basis for planning, executing, and documenting this systematic review. QUADAS-2 was used to critically analyze the quality of all the selected articles. Results: After excluding ineligible articles, the end synthesis has nine studies (n = 9) for qualitative analysis. All nine evaluated studies were found to be at risk of bias, with high or unclear risk in one or more domains. Three out of nine evaluated studies had unclear concerns regarding the applicability, and the remaining six had low concerns. In all the included studies, the IOSs were reported to have deviations in accuracy compared to the conventional techniques for making digital impressions of post space. Conclusions: The accuracy of IOSs was found to be inversely proportional to the length of post space and directly proportional to the diameter of post space. IOSs, when used adequately in short post spaces, can be an alternative to conventional impression-making for making custom posts and cores. Full article

Background: We aimed to assess the uric acid-to-high-density lipoprotein cholesterol (HDL-C) ratio (UHR) and several other parameters with respect to their performance in detecting recurrence among patients with atrial fibrillation (AF) who underwent ablation. Methods: This retrospective cohort study analyzed data from patients who underwent radiofrequency or cryoablation for paroxysmal, persistent, or long persistent AF between September 2021 and September 2023. After ablation, patients were monitored for 24 h, with an ECG Holter used for symptomatic cases. Follow-up visits occurred at 1, 3, and 12 months. Collected data included demographics, comorbidities, echocardiographic measurements, clinical data, ablation type, medication use, and a comprehensive set of laboratory findings. Results: The study included 163 patients, with AF recurrence in 39 (23.93%) patients. Mean age was 57.49 ± 11.22 years, and 59.51% of participants were male. There was no significant difference between recurrent and non-recurrent groups in terms of age or sex distribution. Univariate analysis showed that recurrent patients had significantly larger left atrium diameter, higher percentages of persistent/long AF, and elevated levels of CRP, uric acid, UHR, and uric acid-to-creatinine ratio (UCR). Logistic regression analysis revealed that high left atrium diameter, long persistent AF presence, high CRP and uric acid levels, and high UCR and UHR values greater than 15.1 were independent predictors of AF recurrence. A UHR value of >15.1 was found to predict recurrence with 61.54% sensitivity and 70.97% specificity. Conclusions: Despite low sensitivity, UHR appears to be an independent biomarker that can predict AF recurrence. Including UHR in future risk assessment tools may be beneficial to enhance their accuracy. Full article

Skin cancer is among the most prevalent cancers globally, emphasizing the need for early detection and accurate diagnosis to improve outcomes. Traditional diagnostic methods, based on visual examination, are subjective, time-intensive, and require specialized expertise. Current artificial intelligence (AI) approaches for skin cancer detection face challenges such as computational inefficiency, lack of interpretability, and reliance on standalone CNN architectures. To address these limitations, this study proposes a comprehensive pipeline combining transfer learning, feature selection, and machine-learning algorithms to improve detection accuracy. Multiple pretrained CNN models were evaluated, with Xception emerging as the optimal choice for its balance of computational efficiency and performance. An ablation study further validated the effectiveness of freezing task-specific layers within the Xception architecture. Feature dimensionality was optimized using Particle Swarm Optimization, reducing dimensions from 1024 to 508, significantly enhancing computational efficiency. Machine-learning classifiers, including Subspace KNN and Medium Gaussian SVM, further improved classification accuracy. Evaluated on the ISIC 2018 and HAM10000 datasets, the proposed pipeline achieved impressive accuracies of 98.5% and 86.1%, respectively. Moreover, Explainable-AI (XAI) techniques, such as Grad-CAM, LIME, and Occlusion Sensitivity, enhanced interpretability. This approach provides a robust, efficient, and interpretable solution for automated skin cancer diagnosis in clinical applications. Full article

Background: The prognostic value of the neutrophil–lymphocyte ratio (NLR) in ischemic stroke remains debated due to cohort variability and treatment heterogeneity across studies. This study evaluates the relationship between admission NLR, stroke severity and 90-day outcomes in patients with anterior circulation large vessel occlusion (LVO) undergoing early, successful revascularization. Methods: A retrospective multicenter study was conducted with 1082 patients treated with mechanical thrombectomy for acute ischemic stroke. The relationship between admission NLR, baseline National Institutes of Health Stroke Scale (NIHSS), 24 h NIHSS and 90-day modified Rankin Scale (mRS) outcomes was analyzed using logistic regression. Results: Admission NLR correlated weakly but significantly with both baseline (p = 0.018) and 24 h (p = 0.005) NIHSS scores, reflecting stroke severity. However, multivariate analysis showed that higher 24 h NIHSS scores (OR 0.831, p = 0.000) and prolonged puncture-to-recanalization times (OR 0.981, p = 0.000) were independent predictors of poor 90-day outcomes, whereas NLR was not (p = 0.557). Conclusions: Admission NLR is associated with stroke severity but does not independently predict clinical outcomes at 90 days in patients achieving early and successful revascularization. These findings underscore the critical role of inflammation in the acute phase of stroke but suggest that its prognostic value for long-term outcomes is limited in this context. Full article

With the development of unmanned aerial vehicle (UAV) technology, the threat of UAV intrusion is no longer negligible. Therefore, drone perception, especially anti-UAV tracking technology, has gathered considerable attention. However, both traditional Siamese and transformer-based trackers struggle in anti-UAV tasks due to the small target size, clutter backgrounds and model degradation. To alleviate these challenges, a novel contrastive-augmented memory network (CAMTracker) is proposed for anti-UAV tracking tasks in thermal infrared (TIR) videos. The proposed CAMTracker conducts tracking through a two-stage scheme, searching for possible candidates in the first stage and matching the candidates with the template for final prediction. In the first stage, an instance-guided region proposal network (IG-RPN) is employed to calculate the correlation features between the templates and the searching images and further generate candidate proposals. In the second stage, a contrastive-augmented matching module (CAM), along with a refined contrastive loss function, is designed to enhance the discrimination ability of the tracker under the instruction of contrastive learning strategy. Moreover, to avoid model degradation, an adaptive dynamic memory module (ADM) is proposed to maintain a dynamic template to cope with the feature variation of the target in long sequences. Comprehensive experiments have been conducted on the Anti-UAV410 dataset, where the proposed CAMTracker achieves the best performance compared to advanced tracking algorithms, with significant advantages on all the evaluation metrics, including at least 2.40%, 4.12%, 5.43% and 5.48% on precision, success rate, success AUC and state accuracy, respectively. Full article

The surface qualities of CAD/CAM multi-layered ceramic and hybrid ceramic materials are critical for superior aesthetics and may be impaired by the application of home bleaching. The aim of this study was to assess how home bleaching affects the surface gloss, translucency parameter (TP), and surface roughness (Ra, Rq, and Rz) of different CAD/CAM multi-layered ceramic and hybrid ceramic dental materials. The two types of innovative ceramics that were tested are ultra-translucent multi-layered (UTML) zirconia and polymer-infiltrated ceramic blocks. The samples were treated using home bleaching agents. Each specimen was tested under bleached and non-bleached conditions. The surface gloss and TP of the specimens were measured using a spectrophotometer. The surface examination was performed using scanning electron microscope (SEM) images, while the average surface roughness values (Ra, Rq, and Rz) were calculated using three-dimensional SEM images obtained by an imaging analysis system. A total of 120 disc-shaped resin composite specimens was distributed randomly according to each material in two main groups (n = 60): a control group immersed in 20 mL distilled water (non-bleached) (n = 30), and a second group treated with 20 mL of a home bleaching agent (Crest 3D White Multi-Care Whitening Mouthwash) for 60 s, twice daily for seven days (bleached) (n = 30). The surface gloss, TP, and surface roughness (n = 10 per test for each group) of each group (bleached and non-bleached) was tested. An independent sample t-test was used statistically to assess the effect of home bleaching on the surface gloss, translucency, and roughness of each ceramic material and to compare the two materials. The significance level was adjusted at p ≤ 0.05. The results of the bleached UTML specimens showed no significant changes regarding surface gloss, TP, and roughness, whereas the bleached Vita Enamic specimens showed a significant reduction in surface gloss and TP and increased surface roughness. Moreover, the UTML specimens showed a significantly higher initial surface gloss and TP, and a reduced surface roughness, contrary to the Vita Enamic specimens. This study concluded that surface gloss retention, translucency, and surface roughness could be negatively influenced when subjected to home bleaching according to the type and composition of the ceramic materials. Full article

This study introduces a novel approach for predicting the decay levels of potato by integrating an electronic nose system combined with feature-optimized deep learning models. The electronic nose system was utilized to collect volatile gas data from potatoes at different decay stages, offering a non-invasive method to classify decay levels. To mitigate data scarcity and improve training model robustness, a Gaussian Mixture Embedded Generative Adversarial Network (GMEGAN) was used to generate synthetic data, resulting in augmented datasets that increased diversity and improved model performance. Several machine learning and deep learning models, including traditional classifiers (SVM, LR, RF, ANN) and advanced neural networks (CNN, ECA-CNN, CAM-CNN, Conditional CNN), were trained and evaluated. Models incorporating feature-optimized channel attention modules (f-CAM, f-ECA) achieved a classification accuracy of up to 90.28%, significantly outperforming traditional machine learning models (72–77%) and standard CNN models (83.33%). The inclusion of GMEGAN-generated datasets further enhanced classification performance, especially for feature-optimized Conditional CNN models, with an observed increase in accuracy of up to 5.55%. A comprehensive evaluation of the GMEGAN-generated data, including feature mapping consistency, data distribution similarity, and quality metrics, demonstrated that the generated data closely resembled real data, thereby effectively enhancing dataset diversity. The proposed approach shows significant potential in improving classification accuracy and robustness for agricultural quality assessment, particularly in predicting the decay levels of potatoes. Full article

Cotton topping is a crucial aspect of cotton production, inhibiting apical dominance in cotton plants. Existing cotton topping machinery often results in over-topping. To address this challenge, the characteristics of manual topping operations were emulated by incorporating bionic principles to analyze the motions involved. Studying the artificial topping action and the trajectory of hand movements led to the design of a bionic topping manipulator and a trajectory-generating mechanism, serving as the core component of the cotton topping device. A flat-bottomed follower disc cam mechanism was used to facilitate the automatic opening and closing of the manipulator. The cam’s working area was divided, its contour curve selected, and the manipulator’s pulling spring’s action point and length determined. Subsequently, parametric equations for the motion trajectory of the bionic topping manipulator were established. Building on the topping mechanism’s working principle, a mechanical model was developed to analyze the swing of cotton plants. The model demonstrates that the displacement at the free end of the stalk was primarily influenced by its length. A lifter was then designed to reduce plant swing amplitude and orderly distribute its top position. The designed prototype of a single-row cotton bionic topping device was tested and verified through orthogonal tests, using operating speed, rotational speed, and topping depth as test factors. The topping rate and over-topping rate served as the indices for testing. The results indicated an average topping rate of 78.67% and an over-topping rate of 8%. This was achieved at a 0.3 m/s operating speed, a 40 r/min rotational speed, and a 110 mm topping depth. Cotton topping devices demonstrated greater effectiveness in minimizing damage to cotton plants, and future research should focus on enhancing topping rates even further. This study provides a theoretical foundation and test data to support the design of cotton topping machinery, guiding future mechanical improvements and agricultural practices. Full article