Search Results (176)

Background and Objectives: Preheated composite resins have been proposed as an alternative to conventional luting agents due to their improved resistance, color stability, and adaptation. This review aims to critically evaluate the current literature on the use of preheated composites as luting agents exclusively on dentin and enamel, focusing on their mechanical behavior, optical properties, and biological effects, in order to determine whether they provide superior clinical outcomes compared with conventional resin cements. Materials and Methods: A comprehensive literature search from 2015 to 2025 was conducted in accordance with PRISMA-ScR guidelines. Eligible studies included in vitro investigations comparing the preheated composite with other luting agents performed on human, bovine, analog dentin or enamel substrates. Studies meeting these criteria were screened, evaluated, and synthesized. Results: Fifteen studies met the inclusion criteria: nine focused on the mechanical performance, and the remaining six studies examined additional properties such as color stability, pulpal temperature changes during preheating, film thickness characteristics, and the influence on marginal discrepancy. Conclusions: Preheated composite resins offer improved mechanical properties, marginal adaptation, and fracture resistance compared with conventional luting agents. However, their performance is highly technique-sensitive, and clinical outcomes depend on operator skill, restoration thickness, and material selection. Preheating generally does not compromise color stability, but it can elevate pulpal temperature, particularly when residual dentin is thin. Overall, preheated composites have potential clinical advantages, provided that careful handling and appropriate application are ensured. Full article

Reliable biological datasets, especially those integrating biotic indices such as the Saprobic Index, are scarce, limiting machine and deep learning applications in aquatic ecosystem assessments. This study evaluates Conditional Tabular Generative Adversarial Networks (CTGANs) for generating synthetic datasets that combine physico-chemical parameters with a biological index (Saprobic Index) from multiple monitoring stations in the lower Danube River. Beyond univariate distributional agreement, we assess whether ecologically meaningful multivariate relationships are preserved in the synthetic tables. To support this, we propose an ecology-oriented validation workflow that combines distributional tests with correlation structure and clustering diagnostics across stations. Real monitoring datasets were statistically modelled and recreated using CTGANs, then qualitatively assessed for realism. Comparisons between synthetic and real data employed box plots, Wilcoxon rank-sum tests, correlation matrices, and K-means clustering across stations. Stable variables, including pH, total dissolved solids, and chemical oxygen demand, were well replicated, showing no significant distributional differences (p > 0.05). Conversely, dynamic parameters such as dissolved oxygen, total nitrogen, and suspended solids exhibited notable discrepancies (p < 0.05). Correlation analyses indicated that several strong associations present in the observed data (e.g., total nitrogen–nitrate and total nitrogen–electrical conductivity) were substantially weaker in the synthetic dataset. Overall, a CTGAN can reproduce several marginal patterns but may fail to preserve key ecological linkages, which constrains its use in ecological relationship-dependent inference. While promising for exploratory modelling and general trend analysis, synthetic data should be applied cautiously for studies involving seasonally influenced, biologically significant parameters. Full article

Background: Despite the availability of contouring guidelines and advanced imaging modalities, interobserver variability (IOV) in the delineation of the planning target volume and organs at risk remains a critical factor influencing treatment quality in radiotherapy. The aim of this study was to examine variations in contour delineation with respect to anatomical landmarks, as well as differences in the inclusion of lymph node levels within the PTV. Methods: Ten senior radiation oncologists from six different institutions participated in the study and contoured PTV1, PTV2 and 16 OARs in a patient with oropharyngeal carcinoma. Interobserver variation was quantified by volume statistics such as mean, standard deviation (SD) and ranges, as well as using coefficient of variance (CoV) and conformity index (CI). Results: High agreement was observed in the inclusion of the ipsilateral lymph node levels Ib–IVa and VIIa+b, whereas notable discrepancies were identified in the delineation inclusion of the cervical triangle group and lateral supraclavicular nodes. Regarding OARs, the greatest variability was observed in the delineation of the left and right inner ear, with volume ranges of 0.12–2.84 cm³ and 0.11–2.38 cm³, respectively. Conclusions: This study reaffirms the presence of significant interobserver variability in the delineation of PTVs and OARs in patients with oropharyngeal carcinoma. Especially inclusion of elective lymph node levels and definition of margins around the gross tumor volume are substantial factors for IOV. By emphasizing structured anatomical assessment as a standard approach, variability can be minimized, treatment consistency enhanced, and ultimately, patient outcomes improved. Full article

Background: Bioactive composite resins combine the esthetic and mechanical properties of resin composites with therapeutic functions such as ion release, remineralization, and caries inhibition. While in vitro studies suggest promising bioactivity, their clinical performance in permanent teeth remains uncertain. Objective: This systematic review and meta-analysis critically appraised randomized controlled trials and prospective clinical studies to determine whether bioactive composites offer superior clinical performance compared to conventional resin composites and glass ionomer-based materials. Methods: Electronic databases (PubMed/MEDLINE, Scopus, Web of Science, Google Scholar) were searched for eligible studies (2018–2025). Clinical outcomes assessed restoration survival, marginal integrity, secondary caries, postoperative sensitivity, and esthetic outcomes (color match). Data were pooled using a random-effects model, and risk of bias was assessed with Cochrane criteria. Results: Twenty-two trials met the inclusion criteria. No significant differences were found between bioactive and control restorations for survival/retention (RD = 0.01; 95% CI, –0.01 to 0.03), marginal adaptation (RD = 0.02; 95% CI, –0.02 to 0.06), secondary caries (RD = 0.01; 95% CI, –0.01 to 0.03), or postoperative sensitivity (RD = 0.01; 95% CI, –0.02 to 0.04), with negligible heterogeneity (I² = 0–4%). For color match, glass ionomer restorations showed significantly poorer outcomes (RD = –0.23; 95% CI, –0.31 to –0.14; p < 0.00001; I² = 98%), while conventional resin composites had a slight but significant advantage over bioactive composites (RD = 0.07; 95% CI, 0.02 to 0.12; p = 0.003; I² = 76%). Most studies presented moderate risk of bias and short-term follow-up (<36 months). Conclusions: Current evidence indicates that bioactive composites perform comparably, but not superior, to conventional restoratives in permanent teeth. The discrepancy between laboratory bioactivity and clinical effectiveness highlights the need for long-term, well-designed clinical trials with standardized outcome reporting. Full article

Bearing fault diagnosis is essential for ensuring the safe and reliable operation of rotating machinery. However, accurate and timely fault identification with limited data remains a significant challenge. This study proposes a novel node-incremental-based multisource domain adaptation (NiMDA) approach for bearing fault diagnosis. The method employs a cloud model to adaptively extract fault-sensitive information while accounting for uncertainties across multiple wavelet packet decomposition levels. Subsequently, node incremental domain adaptation (NiDA) is used to construct a base classifier utilizing limited labeled data from both target and source domains. This approach reduces discrepancies between marginal and conditional distributions across different domain feature spaces during the node-increment process, resulting in a compact domain-adaptation structure. Robust diagnostic performance is achieved through parallel ensemble learning of NiDAs across multiple source domains. The experimental results demonstrate that NiMDA significantly outperforms state-of-the-art bearing fault diagnosis methods in few-shot scenarios, achieving improvements of 30.52%, 42.31%, 10.31%, 26.08%, 25.59%, and 7.98% over WDCNN, MCNN-LSTM, Bayesian-RF, DM-RVFLN, Five-shot, and ESCN, respectively, while maintaining satisfactory diagnostic speed. Full article

The restoration of endodontically treated teeth remains a clinical challenge, particularly when substantial coronal tissue loss is present. Endocrowns fabricated using CAD/CAM technologies offer a conservative and esthetic alternative to conventional post-core systems; however, their long-term performance may be influenced by age-related mechanical and thermal stresses. This study evaluated the effect of thermomechanical aging on the marginal adaptation and fracture resistance of endocrowns fabricated from three CAD/CAM materials: zirconia-reinforced lithium silicate (ZLS), polyetherether ketone (PEEK), and 3D-printed resin. Sixty extracted human molars were endodontically treated and restored with endocrowns produced from these materials (n = 20 per group) and then subdivided into aged (n = 10) and control (n = 10) subgroups. Thermomechanical aging involved 5000 thermal cycles between 5 °C and 55 °C, and 75,000 mechanical loading cycles at 50 N. Marginal gaps were examined using scanning electron microscopy, and fracture resistance was tested under axial load at a crosshead speed of 0.5 mm/min. Data were analyzed using two-way ANOVA followed by Tukey’s post hoc test (α = 0.05). Thermomechanical aging significantly increased the marginal gaps in all materials (p < 0.05). The smallest marginal discrepancies were observed in the 3D-printed resin group, while the largest occurred in the ZLS after aging, likely due to dimensional changes during crystallization. Fracture resistance decreased in ZLS (−21.2%) and 3D resin (−20.9%) after aging (p < 0.05) but was not significantly affected in PEEK (−5.4%, p = 0.092). Thermomechanical aging adversely affects marginal adaptation across all materials, whereas its impact on strength is material-dependent. PEEK demonstrated the most stable mechanical performance and may represent a promising alternative for long-term endocrown restorations. Full article

This in vitro investigation evaluated the marginal fit of three pressable glass-ceramic onlay materials: a conventional monolithic lithium disilicate (IPS e.max Press, EM, ivoclar vivadent AG, Schaan, Liechtenstein) and two zirconia-reinforced glass-ceramics (Vita Ambria, VA, VITA Zahnfabrik, Bad Säckingen, Germany; Celtra Press, CP, Sirona Dentsply, Milford, CT, USA). A typodont maxillary first premolar was prepared for an intensive onlay design by a single operator using a milling surveyor. The master die was duplicated with silicone impressions to create 72 identical epoxy resin dies. Seventy-two onlays (n = 24 per material) were fabricated and adhesively cemented to their respective dies. Vertical marginal gaps were recorded under a stereo-electron microscope before and after thermomechanical loading (TML) in a chewing simulator. Data were analyzed with one-way ANOVA and Tukey’s post hoc tests for intergroup comparisons and paired t-tests for pre- versus post-TML values. All groups showed a significant increase in marginal gap following TML. VA exhibited mean gaps of 46.41 µm before and 57.28 µm after loading (p = 0.001). EM demonstrated 41.16 µm before and 46.63 µm after TML (p = 0.002). CP showed 45.70 µm before and 55.99 µm after TML (p = 0.003). Among the three materials, EM maintained the most accurate marginal adaptation both before and after simulated chewing. Despite the increases, all post-loading values remained within the clinically acceptable threshold for marginal discrepancy. These findings indicated that thermomechanical fatigue adversely affected the marginal integrity of pressable glass-ceramic onlays, including zirconia-reinforced formulations. Nevertheless, zirconia-reinforced ceramics (VA and CP) achieved marginal gaps comparable to conventional lithium disilicate and remained within acceptable clinical limits. IPS e.max Press provided the best overall fit, suggesting it may offer superior long-term marginal stability for onlay restorations. Full article

Severe discrepancy between availability of donor organs suitable for clinical transplantation and the proportion of patients on the waiting list has resulted in several clinical problems. First, waiting times for a suitable organ match have become increasingly long, leading to higher mortality while awaiting transplantation. Second, to address this issue, more “marginal” donor lungs have been used in the last two decades, inevitably leading to higher risk of perioperative and long-term complications. The ex vivo lung perfusion (EVLP) technology has been used to recondition marginal donor organs for clinical transplantation. There remains a further untapped pool of donor organs that are currently deemed too injured even for reconditioning via currently available EVLP strategies and are therefore discarded without reconditioning attempts. As the clinical use of EVLP has reached its full potential, further adjunct technologies, such as selective NET removal, cytokine removal and cell therapy techniques, may improve reconditioning outcomes and lead to increased number of donor organs transplanted. Moreover, NET removal may significantly improve donor organ quality and, therefore, the outcomes of recipients after lung transplantation. Such adjunct technology may also provide short- and longer-term benefits in reduction in early graft failure (primary graft dysfunction, PGD) and longer-term chronic lung allograft dysfunction (CLAD, previously known as chronic rejection) via more favorable early immune priming of organs. In this article we present current evidence and future perspectives on this novel intervention strategy that can be used on human donor lungs with the view to increase the utilization rate in lung transplantation in the near future. Full article

Fault identification is vital for geological structure analysis and the optimization of oil–gas extraction. Deep neural networks, especially U-Net and its variants, are widely used for seismic fault interpretation. However, when applied to 3D seismic data volume, these models typically require substantial computation resources and memory consumption. For one reason, they do not take into consideration the obvious differences in characteristics of seismic data in space and time dimensions; therefore, they require a huge number of parameters to capture inherent information for seismic fault detection. This paper presents a lightweight 3D seismic fault interpretation network based on a spatial–temporal asymmetric convolution set (STA-Fault3D) to mitigate the aforementioned issue. STA-Fault3D uses the spatial–temporal asymmetric convolution set to construct a lightweight network and take into consideration seismic data dimension discrepancies. Multi-scale feature fusion operation and an enhanced-training workflow are adopted to improve the performance of the network on field data. Compared with the classic model, FaultSeg3D, it demonstrates improved performance on fault detection continuity with only 12.33% of the parameters and 18.57% of the computational quantity. Compared with the state-of-the-art (SOTA) lightweight network, Fault3DNnet, it reduces parameters by 10% and computational quantity by 4.2% for marginally improved detection results. Full article

Introduction: We aimed to clarify the influence of the thrombus composition on ischemic stroke endovascular thrombectomy (EVT) efficiency by utilizing various staining methods for patients that presented with occlusions of the middle cerebral artery (MCA). Methods: Between September 2017 and May 2021, we analyzed thrombi retrieved during endovascular thrombectomy EVT in patients with acute ischemic stroke due to middle cerebral artery (MCA) occlusion. Patients with reperfusion failure, intracranial atherosclerotic occlusions, and inadequate staining were excluded. The thrombus composition was stratified using three staining techniques—Hematoxylin and Eosin (H&E), Martius Scarlet Blue (MSB) staining, and immunohistochemistry (IHC) for red blood cells (RBCs), white blood cells (WBCs), fibrin (Fibrin II), and platelets (CD41). Associations between EVT efficiency outcomes and the thrombus composition were evaluated. Results: During the study period, thrombus was available for analysis in 159 patients. A total of 59 patients were included in the main analysis. Increases in the trichotomized RBS tertiles were associated with decreases in the components of various platelet/other components but not for fibrin. A modified first pass effect (mFPE) of the modified Thrombolysis in Cerebral Infarction perfusion scale (mTICI) 2b or higher was associated with larger thrombus surface area (16.0 ± 11.6 vs. 47.4 ± 62.3 mm², p = 0.005), a higher MSB fibrin content (29.8 ± 10.7 vs. 21.3 ± 10.9%, p = 0.002), and IHC fibrin (28.5 ± 14.5 vs. 20.1 ± 11.4%, p = 0.008). There was a marginal association between the mTICI 2b mFPE and lower MSB platelet/other components (27.6 ± 20.9 vs. 34.4 ± 14.9%, p = 0.078). The discrepancy between MSB platelet/others and IHC platelets was greater in the mFPE (-) group, suggesting that components other than platelets may contribute to EVT resistance. A mFPE of mTICI 2c or higher was associated with greater thrombus surface area (17.8 ± 11.9 vs. 37.7 ± 55.0 mm², p = 0.015) and MSB fibrin (32.1 ± 10.3 vs. 22.8 ± 11.0%, p = 0.002). There was a marginal reverse association between the mTICI 2c mFPE and MSB RBCs (33.4 ± 20.2% vs. 41.5 ± 17.3%, p = 0.062). There was no significant association between final near-complete reperfusion and the thrombus composition. Conclusions: In patients presenting with occlusions of the MCA, a higher thrombus fibrin content is associated with better EVT efficiency. Both a higher MSB platelet/other components and RBC content may have a negative influence on EVT efficiency. These results may help identify preprocedural biomarkers beyond the conventional assessment of RBCs, WBCs, and fibrin compositions, which could guide decision-making during mechanical thrombectomy. Full article

Background: Chronic kidney disease (CKD) requires strict dietary management tailored to disease stage and individual needs. Recent advances in artificial intelligence (AI) have introduced chatbot-based tools capable of generating dietary recommendations. However, their accuracy, personalization, and practical applicability in clinical nutrition remain largely unvalidated, particularly in non-Western settings. Methods: Simulated patient profiles representing each CKD stage were developed and used to prompt GPT-4 (OpenAI), Gemini (Google), and Copilot (Microsoft) with the same request for meal planning. AI-generated diets were evaluated by three physicians using a 5-point Likert scale across three criteria: personalization, consistency with guidelines, practicality, and availability. Descriptive statistics, Kruskal–Wallis tests, and Dunn’s post hoc tests were performed to compare model performance. Nutritional analysis of four meal plans (Initial, GPT-4, Gemini, and Copilot) was conducted using both GPT-4 estimates and manual calculations validated against clinical dietary sources. Results: Scores for personalization and consistency were significantly higher for Gemini and GPT-4 compared with Copilot, with no significant differences between Gemini and GPT-4 (p = 0.0001 and p = 0.0002, respectively). Practicality showed marginal significance, with GPT-4 slightly outperforming Gemini (p = 0.0476). Nutritional component analysis revealed discrepancies between GPT-4’s internal estimations and manual values, with occasional deviations from clinical guidelines, most notably for sodium and potassium, and moderate overestimation for phosphorus. Conclusions: While AI chatbots show promise in delivering dietary guidance for CKD patients, with Gemini demonstrating the strongest performance, further development, clinical validation, and testing with real patient data are needed before AI-driven tools can be fully integrated into patient-centered CKD nutritional care. Full article

The failure behaviour of historic unreinforced masonry (URM) structures is strongly influenced by the properties of bricks and mortar. Over time, degradation processes compromise these materials, with significant effect on structural response and safety. Nevertheless, deterioration effects on the nonlinear behaviour of masonry have been only marginally investigated. This study investigates the mechanical behaviour and failure mechanisms of historic brick masonry with weak and irregular mortar joints, representative of Mediterranean traditional constructions. An extensive experimental programme was conducted on mortars, historic clay bricks, prisms, wallets, and triplet specimens, complemented by in-situ flat jack tests. Results confirm the critical role of mortar quality and joint irregularities in reducing compressive and shear strength and in influencing deformation capacity of historic masonry. The experimental findings served as a basis for the calibration of a Finite Element Model (FEM), subsequently employed to gain deeper insight into the governing failure mechanisms in a real study case. A critical discussion of compression and shear failure criteria is presented, focusing on historic masonry. Experimental and analytical comparisons show major discrepancies in classical criteria, especially with degraded mortars. The study shows that in historic masonry with weak joints, failure is often governed by compression rather than shear. Full article

This paper presents the results of numerical modeling and vibration testing of a nanosatellite’s optical payload, aimed at assessing its mechanical stability under the mechanical impacts of launch. The purpose of the study is to compare finite element modeling (FEM) data with experimental testing to refine the computational model and improve the reliability of mechanical stability predictions. The methodology included an FEM analysis with an average damping coefficient, an adapter blank test, a resonance study with a low-level sinusoidal run, random vibration tests, and a sinusoidal pulse test. The FEM results showed an average yield margin of safety MoS = 2.5 with a minimum MoS = 1.8 in the primary mirror mount area. The adapter blank test confirmed the absence of natural resonances in the operating range. The resonance study revealed modes in the 300–1340 Hz range, with the most pronounced peaks in the secondary mirror bracket (520–600 Hz) and the electronics unit (1030–1100 Hz). A comparison of the root mean square (RMS) acceleration values between calculations and tests revealed discrepancies due to the heterogeneous nature of the damping. The values of ζ determined by the half-power method varied from 0.9% to 4.8%, which confirms the dependence of the damping properties on the frequency and localization of the modes. The obtained results confirmed the structural integrity of the payload, allowed for the localization of structural elements, and substantiated the need to consider actual damping coefficients in FEM models. The presented data can be used to optimize the design and improve mechanical stability during payload integration into the satellite platform. Full article

To tackle the difficulties in cross-domain fault diagnosis for rolling bearings, researchers have devised numerous domain adaptation strategies to align feature distributions across varied domains. Nevertheless, current approaches tend to be vulnerable to noise disruptions and often neglect the distinctions between marginal and conditional distributions during feature transfer. To resolve these shortcomings, this study presents an innovative fault diagnosis technique for cross-domain applications, leveraging the Attention-Enhanced Residual Network (AttenResNet18). This approach utilizes a one-dimensional attention mechanism to dynamically assign importance to each position within the input sequence, thereby capturing long-range dependencies and essential features, which reduces vulnerability to noise and enhances feature representation. Furthermore, we propose a Dynamic Balance Distribution Adaptation (DBDA) mechanism, which develops an MMD-CORAL Fusion Metric (MCFM) by combining CORrelation ALignment (CORAL) with Maximum Mean Discrepancy (MMD). Moreover, an adaptive factor is employed to dynamically regulate the balance between marginal and conditional distributions, improving adaptability to new and untested tasks. Experimental validation demonstrates that AttenResNet18 achieves an average accuracy of 99.89% on two rolling bearing datasets, representing a significant improvement in fault detection precision over existing methods. Full article

Precipitation estimation is one of the main inputs of hydrological applications, agriculture, and disaster management, but satellite-based precipitation datasets often present biases and discrepancies compared to ground measurements, particularly for data-scarce regions. The present work discusses the development of a novel methodology that merges quantile mapping with machine learning-based spatial clustering, aiming at enhancing the accuracy and reliability of satellite precipitation data. Results showed that quantile mapping, by aligning the distributional properties of satellite data with in situ measurements, reduced systematic biases. On the other hand, quantile mapping could not capture the extremes in precipitation merely by relying on a simple model complexity–performance trade-off. While increasing the number of clusters enhanced capturing spatial heterogeneity and extreme precipitation events, the benefit from using more clusters was really realized up to a point, as continued improvement in metrics beyond 10 clusters was marginal. Conversely, the extra clusters further did not provide any significant reductions in RMSE or Bias. This showed that the effect of further refinement in model performance showed diminishing returns. This hybrid quantile mapping and clustering framework provides a robust tool that can be adapted for enhancing satellite-based precipitation estimates and therefore has implications for data-poor areas where accurate precipitation information is key to sustainable water resource management, climate-resilient agricultural production, and proactive disaster preparedness that supports long-term environmental and socio-economic sustainability. Full article